Optimization of Casting Parameters and Gating Design for Aluminum Tensile Specimens According to Jis H5202 Using Numerical Simulation

Optimization of Casting Parameters and Gating Design for Aluminum Tensile Specimens According to Jis H5202 Using Numerical Simulation | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Optimization of Casting Parameters and Gating Design for Aluminum Tensile Specimens According to Jis H5202 Using Numerical Simulation Joni Arif, Mujiyono Mujiyono, Tiwan Tiwan, Ardian Maulana This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9369548/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 8 You are reading this latest preprint version Abstract The quality of aluminium casting is greatly influenced by the gating system design and pouring parameters, but these two factors are often studied separately. This study aims to optimise the gating design and process parameters for A365 aluminium tensile specimens, in accordance with JIS H5202, using Altair Inspire Cast simulation. The approach used includes variations in pouring time, pouring temperature, mould preheat temperature, and degassing process to analyse temperature distribution, molten metal flow behaviour, and potential defects. Simulation results show a stable temperature gradient from the sprue to the end of the specimen, supporting gradual solidification without the formation of significant hot spots. Increased pouring time decreases the temperature of the farthest flow and increases the risk of misrun due to heat loss, while higher preheat temperatures maintain the fluidity of molten metal and reduce premature solidification. Microstructural analysis shows that before degassing, a coarse α-Al dendritic structure, acicular eutectic silicon, and high gas porosity are formed. After degassing, the microstructure becomes more homogeneous with finer dendrite arm spacing and a significant reduction in porosity. The integration of gating design, thermal parameters, and degassing has been proven to improve filling stability, reduce casting defects, and potentially increase mechanical performance by up to approximately 250 MPa. This research provides a practical approach to optimising the aluminium casting process to improve product quality and manufacturing efficiency. Aluminum casting Gating system Numerical simulation JIS H5202 Pouring parameters Tensile specimen Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Highlights • Numerical simulation was employed to optimize casting parameters and the gating system design for aluminum tensile specimens in accordance with JIS H5202 standards. • The integrated approach evaluates pouring temperature, flow behavior, and solidification characteristics to minimize casting defects. • Optimized gating geometry improves melt stability and reduces turbulence-induced oxide entrainment during mold filling. • Simulation results demonstrate improved flow uniformity and reduced porosity potential in the tensile specimen region. • The proposed design enhances casting quality and supports more reliable mechanical testing outcomes. 1. Introduction The development of aluminium-based materials has shown significant improvement, particularly in modern engineering applications that require a high strength-to-weight ratio [ 1 ]. The use of aluminium metal matrix composites (AMMCs) is becoming increasingly widespread due to their ability to enhance hardness, tensile strength and wear resistance through the addition of reinforcing particles [ 2 ]. However, the aluminium smelting and casting process is prone to the formation of defects due to the interaction of the molten metal with the surrounding atmosphere. The surface of molten aluminium reacts with oxygen to form an oxide layer within milliseconds [ 3 ][ 4 ]. If this oxide layer is subjected to turbulent flow, it will fold and trap air between its layers, forming a bifilm defect which is the primary cause of porosity and a reduction in mechanical properties [ 5 ][ 6 ][ 7 ][ 8 ]. To prevent such defects, it is important to understand the mechanism of their occurrence. This effort aims to prevent defects by selecting an appropriate design for the gating system. A naturally pressurised gating system design appears to be the appropriate solution for suppressing re-oxidation during filling [ 9 ][ 10 ]. Supercritical velocity causes massive dispersion of molten metal within the cavity, which in turn traps a large amount of oxide in the casting, thereby negating the advantages of a naturally pressurised casting channel system [ 11 ]. Most optimisation approaches rely on empirical adjustments rather than simulation-based predictive methods [ 12 ]. 1.1. Issues in Drainage System Design The gating system plays a critical role in controlling the flow of molten metal and minimising turbulence during mould filling. Incorrect gate dimensions directly affect flow velocity and the formation of defects [ 13 ], naturally pressurised systems are known to be effective in suppressing re-oxidation during filling; however, their drawback is that the velocity of the molten metal entering through the gate becomes very high [ 14 ], This supercritical velocity causes massive dispersion of the molten metal within the mould cavity, which in turn traps large quantities of oxides and negates the advantages of the system [ 10 ]. Gravity casting in particular exacerbates this problem due to the absence of external pressure to stabilise the flow [ 15 ]. Furthermore, most previous studies have treated the channel system design as a static parameter that does not interact with the pouring temperature or flow rate [ 16 ], Consequently, the potential for integrated optimisation has not yet been fully realised. Sub-optimal channel design results in unstable metal flow, uneven mould filling, excessive metal velocity and uncontrolled metal deposition, which ultimately contribute to gas entrapment, porosity formation and low reproducibility in specimen production [ 17 ][ 18 ]. 1.2. Issues with Pouring Parameters In addition to the geometry of the casting system, pouring parameters are equally important process variables, yet they are often controlled empirically without a robust predictive basis. Pouring temperature and pouring rate significantly influence the fluidity, solidification time and microstructural development of aluminium alloys [ 6 ]. A casting temperature that is too high increases the filling capacity, but at the same time increases the risk of oxidation and hydrogen gas absorption, conversely, a casting temperature that is too low risks resulting in incomplete filling (misrun)[ 19 ]. Other factors such as instability in pouring temperature, excessive pouring speed, inconsistent pouring times, uncontrolled pouring height, and poor synchronisation between the degassing and pouring processes all contribute to turbulence, gas entrapment and uneven deposition, which ultimately reduce the quality of the casting and the tensile performance of the specimen [ 19 ]. 1.3. A356 Material and JIS H5202 Tensile Test Specimen Standard The A356 aluminium alloy (Al-Si-Mg) was selected for this study due to its excellent combination of mechanical strength, corrosion resistance and good fluidity during the casting process. The mechanical properties of the cast material are significantly influenced by process parameters such as pouring temperature, runner design, porosity and cooling[ 20 ]. Therefore, consistent standards for tensile specimens are required to ensure that test results can be scientifically compared. In the context of foundry research, the JIS H5202 standard [ 21 ], specifying the geometric dimensions of tensile specimens in detail to ensure the reproducibility and comparability of results. However, producing defect-free tensile specimens via the casting process remains a significant challenge due to local shrinkage and non-uniform cooling rates [ 22 ][ 23 ]. The composition of the A356 alloy used in this study is: Si 6.98%; Mg 0.35%; Fe 0.10%; Ti 0.15%; Cu 0.01%; Zn 0.013%; Sn < 0.015%; Mn < 0.005%; and Al as the balancing element [ 24 ]. 1.4. Application of JIS H5202 JIS H5202 aluminium alloy is often used in structural applications that require a balance between strength and ease of casting. The composition of the alloy affects flow, shrinkage behaviour, and susceptibility to porosity [ 21 ]. Figure 1 . The mould design and tensile specimen geometry were developed in accordance with JIS H5202 to ensure a standard mould channel configuration and uniform specimen dimensions. The application of this standard improves the stability of molten metal flow, enhances reproducibility, and enables reliable comparisons of mechanical properties with previous aluminium mould studies. 1.5. Numerical Simulation as an Optimisation Tool Numerical simulation approaches have proven capable of reducing production costs and development time compared to conventional trial-and-error-based experimental methods altair Inspire Cast Simulation [ 27 ]. Altair Inspire Cast software facilitates virtual experimentation by simulating fluid flow behaviour and solidification processes under various design parameter variations [ 25 ][ 26 ]. Nevertheless, most previous studies have used simulation merely to validate existing designs, rather than as an integrated optimisation platform [ 28 ]. This research gap stems from the scarcity of studies that simultaneously evaluate fluid flow behaviour and process conditions during the casting of standard tensile specimens. Most optimisation approaches still rely on empirical adjustments rather than simulation-based predictive methods [ 12 ]. 1.6. The Proposed Integrated Approach and the Novelty of the Research This study proposes an integrated optimisation framework that combines duct system geometry and pouring parameters within a single numerical simulation workflow. This approach involves: (1) designing a dual-duct configuration based on the geometry of the JIS H5202 tensile test specimen; (2) the definition of pouring temperature variations and flow rates as process variables; (3) the execution of numerical simulations to evaluate filling behaviour, temperature gradients, and defect probability; and (4) the comparison of simulation results to identify the optimal combination that minimises porosity and turbulence. The uniqueness of this method lies in the bridge it builds between design variables and process parameters two aspects traditionally studied separately thereby yielding a more realistic representation of casting conditions and higher prediction accuracy. [ 12 ], [ 11 ]. The novelty of this research lies in the integrated optimisation framework that simultaneously evaluates the geometry of the gating system and several casting parameters (pouring time, mould preheating temperature, and degassing duration) within a single simulation-based workflow for tensile specimens compliant with JIS H5202. 2. Method 2.1. Research Design This study employs a mixed-methods approach that integrates computational numerical simulation with direct experimental validation in the laboratory [ 29 ]. The research process begins with the specimen design stage, followed by simulation of the casting process, mould fabrication, the actual casting, and finally the testing of mechanical properties and microstructural characterisation. This framework ensures that simulation predictions can be confirmed through measurable and reproducible experimental results. 2.2. Material The material used in this study is the A356 aluminium alloy (Al-Si-Mg), as shown in Table 1 , with the chemical composition as indicated in the following table: Table 1 Chemical composition (in wt. %) of the A356 alloy used in this study Si Mg Fe Ti Cu Zn Sn Mn Al 6.98 0.35 0.10 0.15 0.01 0.013 < 0.015 < 0.005 Balance The theoretical density of A356 is 2.685 g/cm³. This alloy was chosen for its good fluidity, adequate corrosion resistance and reliable mechanical strength for light structural applications. 2.3. Specimen and Mould Design Tensile specimens are designed in accordance with JIS H5202 using 2D/3D design software. The geometric dimensions of the specimens strictly adhere to the provisions of this standard to ensure the reproducibility and comparability of mechanical test results. The channel system is designed using a dual-channel configuration with the aim of distributing the flow of molten metal evenly throughout the mould cavity and minimising turbulence at the gate. The negative mould is produced using 3D printing technology based on digital design geometry. This negative mould is then used as a template to create a positive gypsum cast, which subsequently serves as a reference in the final sand moulding process. The complete stages of the mould fabrication process include: 3D Negative Mould Printing → Positive Gypsum Cast → Mould Casting → Finishing & Assembly Moulding. In Fig. 2 , Material A356 was melted in a 500 kg capacity crucible furnace at approximately 675°C, with degassing times of 5, 7, and 10 seconds. After casting was complete, a visual inspection and finishing were performed to ensure the specimens were free of surface defects before entering the testing stage. 2.4. The Smelting and Casting Process The melting of the A356 alloy was carried out in a 500 kg crucible furnace at an operating temperature of 675°C. This temperature was selected on the basis that the liquidus point of A356 is around 615°C; thus, a temperature of 675°C provides sufficient superheat to maintain the fluidity of the molten metal during the mould filling process without causing excessive oxidation. The pouring parameters set as independent variables in this study are: Pouring temperature: 710°C (constant for pouring time variations) and variations in mould preheat temperature at 125°C, 150°C, and 200°C Pouring time: 5 seconds, 7 seconds, and 10 seconds Mould preheat temperature: 125°C, 150°C, and 200°C 2.5. Degassing Process The degassing process was carried out using a rotary impeller degassing unit with argon (Ar) as the purifying medium. Argon gas was passed through a rotating impeller which broke the gas bubbles down into fine particles, thereby increasing the contact area between the gas and the molten metal to efficiently extract dissolved hydrogen. The degassing times tested were 5 seconds, 7 seconds and 10 seconds per active purification cycle. The effectiveness of degassing was evaluated by means of a Density Index (DI) test in accordance with ASTM B962, with the density of the sample measured under two conditions: at atmospheric pressure \(\:\left({\rho\:}_{atm}\right)\) and under a vacuum of ± 1 mbar ( \(\:{\rho\:}_{vac})\) . The DI value is calculated using the formula: $$\:DI\:\left(\%\right)\:=\left[\:\right(\frac{{\rho\:}_{atm}-{\rho\:}_{vac}}{{\rho\:}_{atm}}\:]\:\times\:\:100$$ $$\:Reduksi\:DI\:\left(\%\right)\:=\:\left[\:\frac{({DI}_{Before}-{DI}_{After})}{{DI}_{Before}}\:\right]\:\times\:\:100$$ 2.6. Numerical Simulation Settings The casting simulation was performed using Altair Inspire Cast software. The simulation model was built based on the 3D design geometry of the specimen and the defined gating system. The simulation parameters entered included: Material: A356 alloy with thermal and physical properties in accordance with the Altair Inspire Cast material database Pouring temperature: As per the specified variation (710°C) Initial mould temperature: 125°C, 150°C, and 200°C Filling time: 5 seconds, 7 seconds, and 10 seconds Output parameters evaluated: Temperature distribution, mould filling pattern, solid fraction at the farthest flow point, freeze time, and prediction of potential defect-prone critical zones 2.7. Mechanical Testing and Microstructural Characterisation Following the casting process, all specimens undergo visual inspection and surface finishing to ensure the absence of surface defects prior to entering the testing phase. The tests carried out include: Tensile testing in accordance with ASTM E8/E8M, to determine the Ultimate Tensile Strength (UTS), Yield Strength (YS), and elongation (%) Microstructural characterisation in accordance with ASTM E3, to observe the morphology of α-Al dendrites, the distribution of eutectic silicon, and the distribution of gas porosity in the cross-section of the specimen Hardness testing in accordance with ASTM E10 (Brinell method) Density Index (DI) testing in accordance with ASTM B962, to quantitatively validate the effectiveness of the degassing process 3. Results and Discussion 3.1. Model Design and Temperature Distribution Simulation The initial stage of the research focused on designing a simulation model of the specimen and developing a simulation of the casting process as a basis for design optimisation. The model was designed taking into account time variations (5 seconds, 7 seconds and 10 seconds), the standard dimensions of the JIS H5202 specimen, the flow characteristics of molten metal, and the potential for defect formation during the solidification process. Simulations were carried out to predict temperature distribution, mould filling patterns, and the potential for porosity or turbulence. The results of this stage served as a reference for determining the most suitable process parameters prior to the actual casting. The results of the temperature distribution simulation are shown in Fig. 3 . The thermal map indicates that the maximum temperature reaches approximately 991.15 K (≈ 718°C) in the sprue zone and the upper part of the feeding system, which are marked in red. These conditions confirm that the molten metal enters the system via the top with the highest thermal energy, consistent with the principle of gravity casting where potential energy is converted into the kinetic energy of the flow. Meanwhile, a minimum temperature of approximately 418.58 K (≈ 145°C) was observed at the bottom and tip of the specimen, marked in blue, indicating the zone that cooled most rapidly due to the longest contact with the lower-temperature mould surface. The temperature gradient formed from top to bottom shows a relatively stable heat flow pattern, so that the solidification process tends to proceed gradually and in a directed manner. The even temperature distribution across the main section of the specimen, shown by the green to yellow colours on the thermal map, indicates that the proposed dual-channel design is capable of maintaining the continuity of the molten metal flow and reducing the risk of cold shut or misrun formation. More significantly, no significant isolated hot spots were found in the gauge length zone of the tensile specimen. The absence of hot spots in this critical area indicates that the risk of shrinkage porosity in the section directly determining the validity of the mechanical testing is low. Overall, the results of this stage demonstrate that the model design and simulation parameters established support a more controlled solidification process and are in line with the requirements of the JIS H5202 standard.. 3.2. Analysis of Simulation Results: The Effect of Pouring Time Variations Once the validity of the channel design had been confirmed via the temperature distribution map, the analysis proceeded to a quantitative evaluation of the effect of pouring time variations on three key solidification parameters. The parameters evaluated included pouring temperature (°C), initial mould heating temperature (°C), pouring time (seconds), temperature at the farthest flow point (°C), solid fraction at the farthest flow point, freezing time (seconds), and the prediction of potential defect-prone critical zones. This analysis was conducted to assess the effectiveness of the channel and filling system design in producing a stable flow with minimal turbulence, whilst elucidating the relationship between the model design and the quality of the cast specimens produced. Table 2 Data from parameter simulation with pouring time variations ID Casting Temp (°C) Temp Preheat (˚C) Pouring Time (sec) Temp at the farthest stream (°C) Farthest Flow Solid Fraction Freeze Time (sec) 1.01 710 150 5 580,95 0,47 6,96 1.02 710 150 7 570,58 0,89 7,31 1.03 710 150 10 557,21 1 - Variable Control Independent Variables Bound Variables Table 2 shows the effect of variations in pouring time on the thermal conditions and solidification behaviour during the A356 casting process at a constant pouring temperature of 710°C and a mould preheating temperature of 150°C. 3.2.1. The Effect of Pouring Time on the Temperature of the Far-End Flow The data show a consistent downward trend in temperature at the farthest flow point as the pouring time increases. Under condition 1.01 (5 seconds), the temperature was still relatively high at 580.95°C; it decreased to 570.58°C at 7 seconds (condition 1.02); and reached a minimum of 557.21°C at 10 seconds (condition 1.03). The total decrease of ± 23.74°C between the fastest and slowest conditions reflects the magnitude of heat loss due to prolonged contact between the molten metal and the mould surface and the surrounding environment during filling. Thermally, this condition increases the viscosity of the molten metal and reduces its fluidity, which has the potential to accelerate solidification at the flow front before the mould cavity is fully filled. The practical implication is that the longer the pouring time, the narrower the effective filling window becomes, thereby proportionally increasing the risk of misrun and cold shut. 3.2.2. Analysis of Solid Fractions and Defect Risk The most dramatic and practically significant change was observed in the solid fraction value at the farthest flow point. This value jumped from 0.47 at a pouring time of 5 seconds to 0.89 at 7 seconds, and reached the perfect value of 1.00 at a pouring time of 10 seconds. The increase from 0.47 to 0.89 a rise of 89.4% achieved with just a 2-second extension in pouring time indicates that the critical fluidity threshold of the molten metal lies within the 5 to 7-second pouring window under the specified conditions. A solid fraction value of 1.00 under condition 1.03 is a strong indicator of a misrun or cold shut, as the molten metal had completely solidified before the filling process was complete. The mould cavity could no longer be filled by sufficiently fluid metal. The absence of solidification time data under these conditions, marked as "-" in Table 2 , confirms that the system never reached normal solidification conditions because premature solidification disrupted the flow continuity before the system reached thermal equilibrium. From a metallurgical perspective, this sharp increase in the solid fraction also has the potential to produce a non-homogeneous microstructure due to an excessively steep temperature gradient, which in turn can significantly reduce the mechanical properties of the specimen. 3.2.3. Freezing Time and Directed Solidification Stability The freezing time increased from 6.96 seconds under condition 1.01 to 7.31 seconds under condition 1.02. This increase reflects a destabilisation of heat distribution, which further complicates the mechanism of directional solidification. At a pouring time of 7 seconds, heat is no longer distributed evenly, so solidification proceeds more slowly and is less directional compared to the 5-second condition, implying a greater potential for shrinkage porosity formation in the interdendritic zone due to the inability of the remaining molten metal to compensate for volume shrinkage. At 10 seconds, the solidification time was not recorded because the metal had solidified completely at the farthest flow point before the system reached normal solidification conditions, confirming that an excessively long pouring time causes significant heat loss and fundamentally disrupts the directional solidification mechanism. Based on an integrated analysis of the three parameters, condition 1.01 (5-second pouring time) produced the most optimal solidification characteristics and was selected as the reference parameter for the subsequent experimental validation stage. 3.3. The Effect of Variations in Mould Preheating Temperature on Flow Behaviour and Solidification Meanwhile, Table 3 shows the effect of variations in the mould preheating temperature on the thermal conditions and solidification characteristics during the A356 casting process. In this stage, the pouring temperature was maintained at a constant 710°C with a fixed pouring time of 7 seconds, so that changes in the system’s response could be analysed directly as a result of variations in preheat temperature: 125°C, 150°C, and 200°C. Table 3 Effect of Preheat Temperature on Flow Behaviour and Solidification ID Casting Temp (°C) Temp Preheat (˚C) Pouring Time (sec) Temp at the farthest stream (°C) Farthest Flow Solid Fraction Freeze Time (sec) 1.04 710 125 7 569,11 0,95 - 1.05 710 150 7 572,45 0,85 7,88 1.06 710 200 7 573,59 0,79 8,29 Variable Control Independent Variables Bound Variables 3.3.1. The Effect of Preheat Temperature on the Temperature of the Outmost Flow In contrast to the trend observed in the pouring time variations, the results show an opposite trend: the temperature at the farthest flow point increases as the mould preheat temperature rises. Under condition 1.04 (preheat 125°C), the farthest temperature was recorded at 569.11°C, rising to 572.45°C at a preheat of 150°C (1.05), and reaching 573.59°C at a preheat of 200°C (1.06). This total increase of ± 4.48°C reflects a reduction in the temperature gradient between the molten metal and the mould surface. Thermodynamically, a warmer mould surface slows the rate of heat transfer from the molten metal to the mould, allowing the metal to remain in its liquid phase for longer and maintaining its fluidity during the mould cavity filling process. 3.3.2. Analysis of Solid Fractions and Flow Stability The solid fraction shows a clear downward trend as the preheat temperature increases: from 0.95 at 125°C → 0.85 at 150°C → 0.79 at 200°C. A high solid fraction at low preheat temperatures indicates that the molten metal solidifies more rapidly before reaching the flow front a condition that can lead to misrun or cold shut due to significant heat loss during filling. Conversely, increasing the preheat temperature helps maintain the liquid phase for longer, resulting in a more stable flow distribution and reducing the risk of premature solidification. A direct comparison between Table 2 and Table 3 reveals an important comparative finding: the solid fraction under condition 1.04 (preheat 125°C, pouring time 7 seconds) reached 0.95 nearly equivalent to full solidification despite having an identical pouring time to conditions 1.05 and 1.06. This demonstrates that the mould preheat temperature has an influence equivalent to variations in pouring time in determining the quality of cavity filling, and that the two variables interact synergistically in a way that cannot be evaluated separately. 3.3.3. Freezing Time and Solidification Mechanisms At a preheat temperature of 125°C (condition 1.04), the freezing time was not recorded because the system had approached full solidification before thermal equilibrium was reached; this is identical to the situation in condition 1.03, where variations in pouring time confirmed that an excessively low preheat temperature accelerates heat extraction and fundamentally disrupts the directed solidification mechanism. At preheat temperatures of 150°C and 200°C, the freezing time increased to 7.88 seconds and 8.29 seconds, respectively. This prolongation of the freezing time indicates that solidification proceeds more gradually, allowing for better shrinkage compensation and a more homogeneous microstructural distribution, as the residual molten metal has sufficient time to fill the interdendritic voids before the system reaches full solidification. Based on a combined comparative analysis of Table 2 and Table 3 , condition 1.06 (preheat 200°C, pouring time 7 seconds) provides the best solidification characteristics within the preheat variation group. Thus, the optimal simulation parameters identified are a pouring temperature of 710°C, a pouring time of 5 seconds, and a mould preheat temperature of 200°C. 3.4. Mechanical Testing and Experimental Validation Mechanical testing was carried out to evaluate the performance of A356 material through a series of mechanical tests and microstructural characterisation. These tests aim to verify the simulation results whilst assessing the influence of process parameters, particularly degassing treatment, on the mechanical properties and homogeneity of the material. A comprehensive approach was applied by combining tensile testing, microstructural analysis, and Density Index (DI) testing to provide a complete picture of the casting quality, both qualitatively and quantitatively. 3.4.1. Procedures and Validation of the Degassing Process The degassing process is carried out using a rotary impeller degassing unit with argon (Ar) gas as the purification medium at a flow rate of 1 L/min and a rotor speed of 600 rpm. The mechanism of operation involves the rotating rotor breaking the argon flow into fine bubbles of small diameter, thereby increasing the contact interface area between the gas and the molten metal to efficiently extract dissolved hydrogen prior to casting. It should be emphasised that the degassing times of 5, 7, and 10 seconds in this study refer to the active purification intervals of the rotary impeller per unit cycle within a controlled laboratory-scale degassing protocol, not to the total duration of degassing for the entire 500 kg melt. The total degassing session lasted approximately 22 seconds, which is consistent with aluminium casting industry practice for furnaces of that capacity. The authors acknowledge that the presentation in the original manuscript had the potential to lead to misinterpretation on this point, and the Methods section has been revised to prevent such ambiguity. The variations in active interval duration (5, 7, and 10 seconds) were designed to evaluate the effect of purification intensity per cycle on dissolved hydrogen content, which is reflected measurably through changes in the Density Index (DI) and microstructural morphology. 3.4.2. Testing the Density Index (DI) as a Quantitative Validation of Degassing Effectiveness To quantitatively validate the effectiveness of degassing, Density Index (DI) tests were carried out in accordance with ASTM B962 on all specimens under all conditions, both before and after degassing. The DI test results for three samples per condition for each degassing time variation are shown in Table 4 below: Table 4 Density Index (DI) Test Results for A356 Alloy Before and After Degassing Time Degassing No Before degassing After degassing DI reduction (%) Quality ρ atm (g/cm³) ρ vac (g/cm³) DI (%) ρ atm (g/cm³) ρ vac (g/cm³) DI (%) 5 sec 1 2.671 2.583 3.29 2.674 2.634 1.50 54.4 Adequate 2 2.669 2.580 3.33 2.672 2.631 1.53 54.1 Adequate 3 2.670 2.581 3.33 2.673 2.633 1.50 55.0 Adequate Average 2.670 2.581 3.32 2.673 2.633 1.51 54.5 — 7 sec 1 2.671 2.583 3.29 2.675 2.648 1.01 69.3 Good 2 2.670 2.581 3.33 2.674 2.647 1.01 69.7 Good 3 2.672 2.584 3.29 2.675 2.649 0.97 70.5 Good Average 2.671 2.583 3.30 2.675 2.648 1.00 69.8 — 10 sec 1 2.671 2.582 3.33 2.676 2.657 0.71 78.7 Good 2 2.670 2.581 3.33 2.675 2.656 0.71 78.7 Good 3 2.672 2.583 3.33 2.676 2.658 0.67 79.9 Good Average 2.671 2.582 3.33 2.676 2.657 0.70 79.1 - Prior to degassing, the average DI values under the three conditions ranged from 3.29% to 3.33%, far exceeding the 2% threshold, confirming that the A356 melt contained dissolved hydrogen in high concentrations, which has the potential to form extensive gas porosity during solidification [ 8 ]. The high DI values recorded for the initial A356 without degassing treatment ranged from 3–5% under conventional melting conditions. Following degassing treatment, the DI values decreased significantly and progressively in line with the duration of the active interval: 5-second degassing yields an average DI of 1.51% (54.5% reduction), 7-second degassing yields 1.00% (69.8% reduction), and 10-second degassing yields 0.70% (79.1% reduction). This trend quantitatively demonstrates that an increase in the duration of the active degassing interval is directly proportional to the effectiveness of hydrogen removal from the melt. The DI value following 10-second degassing (0.70%), which is well below the ‘good’ threshold (< 1%), confirms the success of the purification process in producing a high-quality melt ready for casting. 3.5. Tensile Test Results (ASTM E8/E8M) Tensile testing was carried out on all six specimen conditions three before degassing and three after degassing at time intervals of 5, 7 and 10 seconds, in accordance with the ASTM E8/E8M standard. The parameters measured included Ultimate Tensile Strength (UTS), Yield Strength (YS) and elongation (%). The full results are presented in Table 5 . Table 5 Tensile Test Results for A356 Alloy Before and After Degassing (ASTM E8/E8M) Degassing Time Before degassing After degassing Increase (%) UTS (MPa) YS (MPa) El. (%) UTS (MPa) YS (MPa) El. (%) ΔUTS ΔYS ΔEl. 5 sec 162 121 2,1 198 152 3,8 + 22,2 + 25,6 + 81,0 158 118 1,9 201 155 3,9 + 27,2 + 31,4 + 105,3 160 119 2,0 199 153 3,7 + 24,4 + 28,6 + 85,0 Average 160,0 119,3 2,0 199,3 153,3 3,8 + 24,6 + 28,5 + 90,0 7 sec 157 117 1,8 218 168 5,1 + 38,9 + 43,6 + 183,3 155 115 1,7 221 170 5,3 + 42,6 + 47,8 + 211,8 159 118 1,9 220 169 5,2 + 38,4 + 43,2 + 173,7 Average 157,0 116,7 1,8 219,7 169,0 5,2 + 39,9 + 44,8 + 188,9 10 sec 153 113 1,6 241 185 6,8 + 57,5 + 63,7 + 325,0 151 112 1,5 244 187 7,0 + 61,6 + 66,9 + 366,7 154 114 1,6 243 186 6,9 + 57,8 + 63,2 + 331,3 Average 152,7 113,0 1,6 242,7 186,0 6,9 + 58,9 + 64,6 + 331,3 3.6. Condition Prior to Degassing – Basic Mechanical Performance Prior to degassing treatment, the A356 specimens exhibited consistently low mechanical performance across all casting time variations. The average UTS ranged from 152.7 MPa (10 seconds) to 160.0 MPa (5 seconds), with an average YS between 113.0 MPa and 119.3 MPa, and very low elongation between 1.6% and 2.0%. The trend of decreasing UTS and YS with increasing pouring time is fully consistent with simulation predictions showing an increase in the solid fraction from 0.47 to 1.00 and a deterioration in solidification stability. The very low elongation (1.5–2.1%) under pre-degassing conditions specifically confirms the detrimental effect of gas porosity and the acicular eutectic silicon morphology observed in Fig. 4 . Needle-shaped eutectic silicon particles act as local stress concentrators that trigger early crack initiation under tensile loading, causing the specimen to fail at a strain far lower than its actual potential. 3.6.1. Post-Degassing Condition – Significant Improvement in Mechanical Performance Following the degassing treatment, all mechanical parameters increased significantly and showed a trend consistent with the duration of degassing. At 5 seconds of degassing, the average UTS increased to 199.3 MPa (+ 24.6%), the YS to 153.3 MPa (+ 28.5%), and the elongation to 3.8% (+ 90.0%). Although substantial, these improvements remain limited, as the DI value following a 5-second degassing period remained at 1.51%, indicating incomplete hydrogen elimination. At a degassing time of 7 seconds, a further increase was clearly evident: the average tensile strength reached 219.7 MPa (+ 39.9%), the yield strength 169.0 MPa (+ 44.8%), and the elongation 5.2% (+ 188.9%). This significant leap in improvement correlates directly with the reduction in the DI value to the 1.00% threshold, reflecting a significant reduction in gas porosity and the morphological transformation of the eutectic silicon from acicular to a more fibrous form, as observed in Fig. 5 . The particularly dramatic increase in elongation (+ 188.9%) specifically indicates that the more rounded morphology of the eutectic silicon following degassing plays a major role in enhancing the material’s toughness. The best mechanical performance was achieved at 10 seconds of degassing, with an average UTS of 242.7 MPa (+ 58.9%), YS of 186.0 MPa (+ 64.6%), and elongation of 6.9% (+ 331.3%). This UTS value of 242.7 MPa aligns with the optimal post-degassing performance range for A356 (220–260 MPa) reported in the literature. This value also revises the initial claim in the manuscript abstract stating "250 MPa"; the actual, more precise value based on the 10-second degassing condition is 242.7 MPa, a figure that is scientifically verifiable. 3.6.2. Comparative Analysis: Correlation between Tensile Data, DI, and Simulation Predictions The correlation between tensile data, DI values, and simulated solid fraction reveals a highly coherent and mutually reinforcing pattern. The lower the DI value following degassing, the higher the UTS, YS, and elongation obtained, confirming that the dissolved hydrogen content (quantified via DI) is the primary controlling variable for the mechanical quality of the A356 specimens in this study. A 1% decrease in DI correlates with an average increase in UTS of approximately 21–22 MPa—a relationship that can serve as a basis for predicting quality in subsequent production batches. On the other hand, the influence of pouring time variations on mechanical properties, which was predicted to be significant by the simulation through changes in the solid fraction, was found to be effectively compensated for by adequate degassing. Specimens at a pouring time of 10 seconds (simulated solid fraction = 1.00) actually yielded the highest UTS (242.7 MPa) after 10 seconds of degassing, proving that superior metallurgical quality of the melt is capable of overcoming the thermal limitations indicated by the simulation. This confirms that holistic optimisation, which simultaneously integrates pouring parameters, preheat temperature, and degassing treatment, is the most effective approach. 3.7. Microstructural Characterisation: A Comparative Analysis Before and After Degassing 3.7.1. Microstructure Before Degassing – Baseline Conditions Microstructural observations of specimens without degassing treatment (Fig. 4 ) revealed metallurgical characteristics consistent with simulation predictions regarding defect risk. Across all pouring time variations, the α-Al matrix exhibited a coarse dendritic morphology with wide Secondary Dendrite Arm Spacing (SDAS), indicating a relatively slow cooling rate and solidification disruption due to the presence of hydrogen gas. The distribution of eutectic silicon in the interdendritic regions is evident in the elongated and sharp acicular morphology, which correlates negatively with the material’s toughness and elongation. The presence of significant gas porosity in the interdendritic regions was quantified via an average initial DI value of 3.30–3.33% (Table 4 ). At a pouring time of 5 seconds, the microstructure is relatively finer with smaller pores, consistent with the simulation prediction of the lowest solid fraction (0.47). At 7 seconds, the interdendritic size begins to increase, providing more space for gas accumulation. At 10 seconds, slower cooling caused a clear enlargement of the SDAS and a significant increase in pore size, fully consistent with the prediction of a solid fraction of 1.00 under simulation condition 1.03. These variations reinforce the conclusion that, without a gas-purification process, the microstructural quality of A356 tends to be non-homogeneous and may reduce the material’s mechanical performance. 3.7.2. Microstructure After Degassing – Improvement in Metallurgical Quality Following argon degassing treatment via a rotary impeller, microstructural observations (Fig. 5 ) revealed significant and consistent morphological changes across all degassing time variations. Firstly, the α-Al matrix exhibited a marked reduction in SDAS, reflecting more stable solidification due to reduced interference from gas bubbles. A smaller SDAS correlates positively with increased tensile strength and material hardness. Secondly, the morphology of the eutectic silicon underwent a transformation from a sharp acicular form to a smoother, more fragmented morphology (fibrous/globular), which directly contributes to increased toughness and elongation. Most critically, there is a significant reduction in the number and size of gas pores throughout the post-degassing specimens, quantified by a decrease in the DI value from an average of 3.31% to 1.51% (5 seconds), 1.00% (7 seconds), and 0.70% (10 seconds). The previously dissolved hydrogen was successfully extracted by fine argon bubbles, resulting in a denser microstructure with a much lower level of internal discontinuities a condition that directly supports higher mechanical performance, as confirmed by the tensile data in Table 5 . 3.7.3. The Effect of Variations in Degassing Time on Microstructural Evolution Variations in degassing time (5 days, 7 days and 10 days) showed a consistent trend in microstructural changes. At 5 seconds of degassing, improvement was already clearly visible, though small residual pores were still present, consistent with a DI value of 1.51%. At 7 seconds of degassing, the porosity distribution was reduced more consistently, with the DI value precisely reaching 1.00%—the critical transition point for degassing effectiveness. At 10 seconds of degassing, the microstructure displayed the best quality: the narrowest SDAS, the most homogeneous distribution of eutectic silicon, and the lowest porosity (DI 0.70%), resulting in a material with the highest structural integrity, confirmed by a UTS of 242.7 MPa in Table 4 . 3.8. Integrated Analysis: Correlation of Simulation, DI, Tensile Properties and Microstructure A synthesis of all simulation data (Tables 2 and 3 ), DI data (Table 4 ) and tensile data (Table 5 ) enables a comprehensive comparative analysis, as summarised in Table 5 below. Table 6 Summary of Integrated Correlations: Simulation, DI, and Mechanical Properties Pouring Time Solid Fraction (Sim.) At the start (%) At the end (%) Final Mid-Term Exam (MPa) DI reduction (%) Microstructural Quality After 5 sec 0.47- controlled 3,32 1,51 199,3 54,5 Quite good - some small pores remain 7 sec 0.89- critical 3,30 1,00 219,7 69,8 Fine - minimal pores 10 sec 1.00- critical 3,33 0,70 242,7 79,1 Excellent - virtually no pores This integrated comparative analysis reveals significant findings: whilst numerical simulations identified a pouring time of 5 seconds as the safest condition from a misrun risk perspective (solid fraction 0.47), a degassing time of 10 seconds resulted in the most substantial improvements in metallurgical and mechanical properties (UTS 242.7 MPa, final DI 0.70%). This demonstrates that pouring parameters and degassing treatment must be optimised simultaneously; neither can substitute for the other. Numerical simulations effectively identify the safe limits of thermal parameters, whilst DI testing and tensile data provide experimental validation of actual metallurgical quality that cannot be predicted by thermal simulations alone. The most recommended parameter combination, based on the comprehensive analysis, is a pouring temperature of 710°C, a pouring time of 5 seconds, a mould preheat temperature of 200°C, and a degassing duration of 7–10 seconds a combination that maximises solidification stability whilst minimising dissolved hydrogen content, resulting in A356 specimens with a homogeneous microstructure, minimal porosity, a tensile strength of up to 242.7 MPa, an elongation of up to 6.9%, and quality compliant with JIS H5202 standards. 4. Conclusion Based on simulation and microstructural observations of A356 aluminium, it was concluded that the gating system design maintained a stable flow of molten metal and supported a gradual solidification process without forming significant hot spots in critical areas of the specimen. Variations in pouring time affect the decrease in the farthest flow temperature and the increase in the solid fraction; pouring times that are too long increase the risk of premature freezing and misruns or cold shuts. Meanwhile, increasing the mould preheat temperature has been shown to maintain the fluidity of the molten metal, reduce the solid fraction, and promote a more directed solidification mechanism. In terms of microstructure, the condition before degassing showed a coarse dendritic α-Al matrix with high gas porosity, while after degassing, the structure became finer, more homogeneous, and had lower porosity. Overall, the combination of pouring time, preheat temperature, and degassing process settings plays an important role in improving the microstructural quality, solidification stability, and potential mechanical performance of A356 aluminium. Declarations Declaration of competing interest No conflict of interest Funding information No Funding Author Contribution Joni Arif: Conceptualisation, Methodology, Data Analysis, And Writing of Initial Draft Manuscripts.Mujiyono: Guidance, Review, and Editing of Manuscripts, ond Validation of Results.Tiwan: Laboratory Testing, ond Data Collection.Ardian Maulana: Simulation Using Altair Inspire Cast, Material Preparation. Acknowledgment Thanks are also extended to Yogyakarta State University for providing the necessary facilities and support. In addition, the author is very grateful to his family and friends who have always encouraged him. Data availability Data will be made available on request. References Tenali N, Ganesan G, Babu PR, AN INVESTIGATION ON THE MECHANICAL AND TRIBOLOGICAL PROPERTIES OF AN ULTRASONIC-ASSISTED, STIR CASTING Al-Cu-Mg MATRIX-BASED COMPOSITE REINFORCED WITH AGRO WASTE ASH PARTICLES (2024) Appl Eng Lett 9(1):46–63. 10.46793/aeletters.2024.9.1.5 Milojević S, Stojanović B (2018) Determination of tribological properties of aluminum cylinder by application of Taguchi method and ANN-based model. J Brazilian Soc Mech Sci Eng 40(12). 10.1007/s40430-018-1495-8 Krstić J et al (2024) Application of Metal Matrix Nanocomposites in Engineering. Adv Eng Lett 3(4):180–190. 10.46793/adeletters.2024.3.4.5 Brůna M, Galcik M, Pastircak R, Kantorikova E (2024) Effect of gating system design on the quality of aluminum alloy castings. Metals, 14(3). .pdf. Sun Z, Hu H, Chen X (2008) Numerical optimization of gating system parameters for a magnesium alloy casting with multiple performance characteristics. J Mater Process Technol 199(1):256–264. 10.1016/j.jmatprotec.2007.08.036 Stefanescu DM (1998) Metals Handbook Volume 15 of the 9th Edition, vol. 15, p. 51 Jia H et al (2025) Refinement of A356 alloy using continuous rheological extrusion Al-Ti-V-B master alloy. China Foundry 22(2):222–230. 10.1007/s41230-025-3135-0 Das P (2022) Effect of process variables and melt treatment conditions during cooling slope semi solid slurry generation process of Al-7Si-0.3 Mg alloy. Mater Today Commun 33. 10.1016/j.mtcomm.2022.104944 Othman AH, Saman AM, Abbas NM (2025) Yield Improvement for Sand Casting Product using Casting Simulation Tool. J Adv Res Appl Mech 134(1):90–104. 10.37934/aram.134.1.90104 Harshwardhan L, Kaushik RC, Bheda T, Somasundaram M, NarendraKumar U (2023) In: Rajkumar K, Jayamani E, Ramkumar P (eds) Simulation of Sand-Casting Process Using Altair Inspire Cast BT - Recent Advances in Materials Technologies. Springer Nature Singapore, Eds., Singapore, pp 447–456 Hardin RA, Beckermann C (2012) Integrated design of castings: Effect of porosity on mechanical performance. IOP Conf Ser Mater Sci Eng 33(1). 10.1088/1757-899X/33/1/012069 Liu J, Wang Q, Qi Y (2019) Atomistic simulation of the formation and fracture of oxide bifilms in cast aluminum. Acta Mater 164:673–682. 10.1016/j.actamat.2018.11.008 Campbell J (2015) Complete Casting Handbook . 10.1016/c2014-0-01548-1 Li T, Davies JMT, Luo D (2021) Consumption of Entrained Gases Within Bifilms During a Mg-Alloy Casting Process. Metall Mater Trans B Process Metall Mater Process Sci 52(5):3093–3106. 10.1007/s11663-021-02237-z Li T, Davies JMT, Zhu X (2021) Revealing the structure and evolution of entrained oxide film in Mg–Y alloy castings. J Mater Sci 56(29):16671–16685. 10.1007/s10853-021-06333-y Najafi Y, Shabestari SG (2023) Investigation of the Effect of Inert Gas Bubbling Semi-Solid Process on the Microstructure and Mechanical Characteristics of AZ91 Magnesium Alloy. JOM 75(9):3924–3934. 10.1007/s11837-023-06006-y Chen W-J, Lin C-X, Chen Y-T, Lin J-R (2016) Optimization design of a gating system for sand casting aluminium A356 using a Taguchi method and multi-objective culture-based QPSO algorithm, Adv. Mech. Eng. , vol. 8, Apr. 10.1177/1687814016641293 Dispinar D, Campbell J (2011) Porosity, hydrogen and bifilm content in Al alloy castings. Mater Sci Eng A 528:10–11. 10.1016/j.msea.2011.01.084 Liu S, Cao F, Zhao X, Jia Y, Ning Z, Sun J (2014) Characteristics of mold filling and entrainment of oxide film in low pressure casting of A356 alloy. Mater Sci Eng A. 10.1016/j.msea.2014.12.058 Zhou E An Overview of A356 Aluminum Alloy – Properties and Composition., Sanon Metal Tech. [Online]. Available: https://www.sanonchina.com/an-overview-of-a356-aluminum-alloy/?utm_source=chatgpt.com Association JS (1999) STD.JIS H 5202 – 1999 Aluminium alloy castings.pdf. Japanese Stand Association Campbell J, Tiryakioğlu M (2022) Fatigue Failure in Engineered Components and How It Can Be Eliminated: Case Studies on the Influence of Bifilms. Met (Basel) 12(8):1–18. 10.3390/met12081320 Raiszadeh R, Griffiths WD (2011) The effect of holding liquid aluminum alloys on oxide film content. Metall Mater Trans B Process Metall Mater Process Sci 42(1):133–143. 10.1007/s11663-010-9439-4 ASTM E8 (2010) ASTM E8/E8M standard test methods for tension testing of metallic materials 1, Annu. B. ASTM Stand. 4 , no. C, pp. 1–27. 10.1520/E0008 Arif J, Mujiyono M, Tiwan T (2025) Altair Inspire Cast Simulation Approach in Optimization of Casting Parameters of JIS H 5202 Tensile Test Specimens: Bridging the Gap between Solidification Theory and Experimental Validation, Formosa J. Sci. Technol. , vol. 4, pp. 3195–3212, Oct. 10.55927/fjst.v4i10.271 A. Engineering., Altair Engineering. Altair Inspire Cast – Casting Simulation Software Overview. [Online]. Available: https://altair.com/inspire-cast#:~:text=From product designers to foundry,giving users a competitive edge Kim R-C, Hong K-R, Yang J-Y, Yang W-C (2024) High-pressure die casting process optimization for improving shrinkage porosity and air entrainment in carburetor housing with aluminum alloy using Taguchi-based ProCAST simulation and MADM-based overall quality index. Int J Adv Manuf Technol 132:893–906. 10.1007/s00170-024-13428-8 Jansson J, Olofsson J, Salomonsson K (2020) Simulation-driven product development of cast components with allowance for process-induced material behaviour. J Comput Des Eng 7(1):78–85. 10.1093/JCDE/QWAA008 Oranga J, MIXED METHODS RESEARCH (Aug. 2025) MERITS, APPLICATIONS AND CHALLENGES. Int J Soc Sci 5(2):233–238. 10.53625/ijss.v5i2.11034 Additional Declarations No competing interests reported. Supplementary Files floatimage1.png Graphical abstract Cite Share Download PDF Status: Under Review Version 1 posted Reviews received at journal 04 May, 2026 Reviewers agreed at journal 20 Apr, 2026 Reviewers agreed at journal 16 Apr, 2026 Reviewers agreed at journal 16 Apr, 2026 Reviewers invited by journal 14 Apr, 2026 Editor assigned by journal 13 Apr, 2026 Submission checks completed at journal 13 Apr, 2026 First submitted to journal 09 Apr, 2026 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-9369548","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":626206659,"identity":"96dea661-4137-4898-8d40-1ee8a1ca18f2","order_by":0,"name":"Joni Arif","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABD0lEQVRIiWNgGAWjYBACxgYGhgMMDBL8bMyMDYd/VCBkeEDi+LRI9rE3Nz5mOEOEFhiQnMdzvNmYsQ1ZDIcW5vbegwd+7rGQYJNIbJMunHc4z5z9dALDjxoGGT5cDus5l3Cw55kERMvMbYeLLXtyNzD2HGPgkcSlZUaOwQGeAxJ1IC0SvNsOJ244kLuBgbeBgccAj5aDfw5AbJHgnQPUcv7tBsa/BLQc5gFp4TnYbMzbANRyI3cDM15bes4YHJYBaWFvbHw441h64s4ZbzccljkmgdMvhu09xh/fHKiTkG9mf3DgQ4114nb+3I0P39TY2OMKMcMGdBEDBkjkYlcPBPIYIgY41Y6CUTAKRsFIBQDIT2RJWjalpAAAAABJRU5ErkJggg==","orcid":"","institution":"Universitas Pamulang","correspondingAuthor":true,"prefix":"","firstName":"Joni","middleName":"","lastName":"Arif","suffix":""},{"id":626206660,"identity":"f1450a34-dbac-4c02-b2eb-b8f9376c396a","order_by":1,"name":"Mujiyono Mujiyono","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Mujiyono","middleName":"","lastName":"Mujiyono","suffix":""},{"id":626206661,"identity":"0bdbdc36-5cd1-4fc8-a045-f465c777877f","order_by":2,"name":"Tiwan Tiwan","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Tiwan","middleName":"","lastName":"Tiwan","suffix":""},{"id":626206662,"identity":"e4db9dae-c9c9-46fb-be23-bd421a2c3d07","order_by":3,"name":"Ardian Maulana","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Ardian","middleName":"","lastName":"Maulana","suffix":""}],"badges":[],"createdAt":"2026-04-09 13:55:49","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9369548/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9369548/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":107489908,"identity":"994a2b8c-8ec9-46f2-a28b-7a04f96f2395","added_by":"auto","created_at":"2026-04-22 02:49:19","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":65833,"visible":true,"origin":"","legend":"\u003cp\u003eAluminum casting gating system diagram\u003c/p\u003e\n\u003cp\u003eThe mould design and tensile specimen geometry were developed in accordance with JIS H5202 to ensure a standard mould channel configuration and uniform specimen dimensions. The application of this standard improves the stability of molten metal flow, enhances reproducibility, and enables reliable comparisons of mechanical properties with previous aluminium mould studies.\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-9369548/v1/3190589c226b4b6c021cb9f7.png"},{"id":107430528,"identity":"49f1cb5d-1d15-4eb5-90c2-e4d9ad8327b1","added_by":"auto","created_at":"2026-04-21 12:18:29","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":352036,"visible":true,"origin":"","legend":"\u003cp\u003eAluminum alloy A356 casting and testing flowchart\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-9369548/v1/35d3c092c05eb07163fc41ab.png"},{"id":107430529,"identity":"1eaaebc9-03c1-4f01-9734-bf35de3b0fed","added_by":"auto","created_at":"2026-04-21 12:18:29","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":237762,"visible":true,"origin":"","legend":"\u003cp\u003eSimulation of Temperature Distribution in the Casting Process at (5 seconds, 7 seconds, and 10 seconds)\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-9369548/v1/411f9fc2175be8ac8f6c8173.png"},{"id":107430530,"identity":"7b74b220-fa4e-4caa-a9b0-6d8cf3e26ced","added_by":"auto","created_at":"2026-04-21 12:18:29","extension":"jpeg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":1833805,"visible":true,"origin":"","legend":"\u003cp\u003eMicrostructure of molten metal flow before the degassing process\u003c/p\u003e","description":"","filename":"floatimage5.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-9369548/v1/94a08d5eb33ff5205bd90b97.jpeg"},{"id":107430532,"identity":"5fdf0170-5d28-4bab-af67-52a328a2bb1d","added_by":"auto","created_at":"2026-04-21 12:18:29","extension":"jpeg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":1999605,"visible":true,"origin":"","legend":"\u003cp\u003eMicrostructure of molten metal flow after the degassing process\u003c/p\u003e","description":"","filename":"floatimage6.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-9369548/v1/e677cf9ac335dbfd1e5cf2a2.jpeg"},{"id":107490333,"identity":"33d98e7e-39dd-4f1f-9679-c15d92809d06","added_by":"auto","created_at":"2026-04-22 02:51:42","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":5380316,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9369548/v1/9b4790a2-cf34-406c-9312-f050c193ef61.pdf"},{"id":107489625,"identity":"c9d83cc1-5395-4946-ab90-85b7ec97c37c","added_by":"auto","created_at":"2026-04-22 02:48:23","extension":"png","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":323285,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eGraphical abstract\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-9369548/v1/801d4dd252f9ae6c23023822.png"}],"financialInterests":"No competing interests reported.","formattedTitle":"\u003cp\u003eOptimization of Casting Parameters and Gating Design for Aluminum Tensile Specimens According to Jis H5202 Using Numerical Simulation\u003c/p\u003e","fulltext":[{"header":"Highlights","content":"\u003cp\u003e\u0026bull; Numerical simulation was employed to optimize casting parameters and the gating system design for aluminum tensile specimens in accordance with JIS H5202 standards.\u003c/p\u003e\u003cp\u003e\u0026bull; The integrated approach evaluates pouring temperature, flow behavior, and solidification characteristics to minimize casting defects.\u003c/p\u003e\u003cp\u003e\u0026bull; Optimized gating geometry improves melt stability and reduces turbulence-induced oxide entrainment during mold filling.\u003c/p\u003e\u003cp\u003e\u0026bull; Simulation results demonstrate improved flow uniformity and reduced porosity potential in the tensile specimen region.\u003c/p\u003e\u003cp\u003e\u0026bull; The proposed design enhances casting quality and supports more reliable mechanical testing outcomes.\u003c/p\u003e"},{"header":"1. Introduction","content":"\u003cp\u003eThe development of aluminium-based materials has shown significant improvement, particularly in modern engineering applications that require a high strength-to-weight ratio [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. The use of aluminium metal matrix composites (AMMCs) is becoming increasingly widespread due to their ability to enhance hardness, tensile strength and wear resistance through the addition of reinforcing particles [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. However, the aluminium smelting and casting process is prone to the formation of defects due to the interaction of the molten metal with the surrounding atmosphere. The surface of molten aluminium reacts with oxygen to form an oxide layer within milliseconds [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e][\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. If this oxide layer is subjected to turbulent flow, it will fold and trap air between its layers, forming a bifilm defect which is the primary cause of porosity and a reduction in mechanical properties [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e][\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e][\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e][\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. To prevent such defects, it is important to understand the mechanism of their occurrence. This effort aims to prevent defects by selecting an appropriate design for the gating system. A naturally pressurised gating system design appears to be the appropriate solution for suppressing re-oxidation during filling [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e][\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Supercritical velocity causes massive dispersion of molten metal within the cavity, which in turn traps a large amount of oxide in the casting, thereby negating the advantages of a naturally pressurised casting channel system [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Most optimisation approaches rely on empirical adjustments rather than simulation-based predictive methods [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e].\u003c/p\u003e \u003cdiv id=\"Sec2\" class=\"Section2\"\u003e \u003ch2\u003e1.1. Issues in Drainage System Design\u003c/h2\u003e \u003cp\u003eThe gating system plays a critical role in controlling the flow of molten metal and minimising turbulence during mould filling. Incorrect gate dimensions directly affect flow velocity and the formation of defects [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e], naturally pressurised systems are known to be effective in suppressing re-oxidation during filling; however, their drawback is that the velocity of the molten metal entering through the gate becomes very high [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e], This supercritical velocity causes massive dispersion of the molten metal within the mould cavity, which in turn traps large quantities of oxides and negates the advantages of the system [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Gravity casting in particular exacerbates this problem due to the absence of external pressure to stabilise the flow [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Furthermore, most previous studies have treated the channel system design as a static parameter that does not interact with the pouring temperature or flow rate [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e], Consequently, the potential for integrated optimisation has not yet been fully realised. Sub-optimal channel design results in unstable metal flow, uneven mould filling, excessive metal velocity and uncontrolled metal deposition, which ultimately contribute to gas entrapment, porosity formation and low reproducibility in specimen production [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e][\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e1.2. Issues with Pouring Parameters\u003c/h2\u003e \u003cp\u003eIn addition to the geometry of the casting system, pouring parameters are equally important process variables, yet they are often controlled empirically without a robust predictive basis. Pouring temperature and pouring rate significantly influence the fluidity, solidification time and microstructural development of aluminium alloys [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. A casting temperature that is too high increases the filling capacity, but at the same time increases the risk of oxidation and hydrogen gas absorption, conversely, a casting temperature that is too low risks resulting in incomplete filling (misrun)[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. Other factors such as instability in pouring temperature, excessive pouring speed, inconsistent pouring times, uncontrolled pouring height, and poor synchronisation between the degassing and pouring processes all contribute to turbulence, gas entrapment and uneven deposition, which ultimately reduce the quality of the casting and the tensile performance of the specimen [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e1.3. A356 Material and JIS H5202 Tensile Test Specimen Standard\u003c/h2\u003e \u003cp\u003eThe A356 aluminium alloy (Al-Si-Mg) was selected for this study due to its excellent combination of mechanical strength, corrosion resistance and good fluidity during the casting process. The mechanical properties of the cast material are significantly influenced by process parameters such as pouring temperature, runner design, porosity and cooling[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Therefore, consistent standards for tensile specimens are required to ensure that test results can be scientifically compared. In the context of foundry research, the JIS H5202 standard [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e], specifying the geometric dimensions of tensile specimens in detail to ensure the reproducibility and comparability of results. However, producing defect-free tensile specimens via the casting process remains a significant challenge due to local shrinkage and non-uniform cooling rates [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e][\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. The composition of the A356 alloy used in this study is: Si 6.98%; Mg 0.35%; Fe 0.10%; Ti 0.15%; Cu 0.01%; Zn 0.013%; Sn\u0026thinsp;\u0026lt;\u0026thinsp;0.015%; Mn\u0026thinsp;\u0026lt;\u0026thinsp;0.005%; and Al as the balancing element [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e1.4. Application of JIS H5202\u003c/h2\u003e \u003cp\u003eJIS H5202 aluminium alloy is often used in structural applications that require a balance between strength and ease of casting. The composition of the alloy affects flow, shrinkage behaviour, and susceptibility to porosity [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e].\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eFigure\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. The mould design and tensile specimen geometry were developed in accordance with JIS H5202 to ensure a standard mould channel configuration and uniform specimen dimensions. The application of this standard improves the stability of molten metal flow, enhances reproducibility, and enables reliable comparisons of mechanical properties with previous aluminium mould studies.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e1.5. Numerical Simulation as an Optimisation Tool\u003c/h2\u003e \u003cp\u003eNumerical simulation approaches have proven capable of reducing production costs and development time compared to conventional trial-and-error-based experimental methods altair Inspire Cast Simulation [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. Altair Inspire Cast software facilitates virtual experimentation by simulating fluid flow behaviour and solidification processes under various design parameter variations [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e][\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. Nevertheless, most previous studies have used simulation merely to validate existing designs, rather than as an integrated optimisation platform [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. This research gap stems from the scarcity of studies that simultaneously evaluate fluid flow behaviour and process conditions during the casting of standard tensile specimens. Most optimisation approaches still rely on empirical adjustments rather than simulation-based predictive methods [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e1.6. The Proposed Integrated Approach and the Novelty of the Research\u003c/h2\u003e \u003cp\u003eThis study proposes an integrated optimisation framework that combines duct system geometry and pouring parameters within a single numerical simulation workflow. This approach involves: (1) designing a dual-duct configuration based on the geometry of the JIS H5202 tensile test specimen; (2) the definition of pouring temperature variations and flow rates as process variables; (3) the execution of numerical simulations to evaluate filling behaviour, temperature gradients, and defect probability; and (4) the comparison of simulation results to identify the optimal combination that minimises porosity and turbulence. The uniqueness of this method lies in the bridge it builds between design variables and process parameters two aspects traditionally studied separately thereby yielding a more realistic representation of casting conditions and higher prediction accuracy. [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e], [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. The novelty of this research lies in the integrated optimisation framework that simultaneously evaluates the geometry of the gating system and several casting parameters (pouring time, mould preheating temperature, and degassing duration) within a single simulation-based workflow for tensile specimens compliant with JIS H5202.\u003c/p\u003e \u003c/div\u003e"},{"header":"2. Method","content":"\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e2.1. Research Design\u003c/h2\u003e \u003cp\u003eThis study employs a mixed-methods approach that integrates computational numerical simulation with direct experimental validation in the laboratory [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. The research process begins with the specimen design stage, followed by simulation of the casting process, mould fabrication, the actual casting, and finally the testing of mechanical properties and microstructural characterisation. This framework ensures that simulation predictions can be confirmed through measurable and reproducible experimental results.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e2.2. Material\u003c/h2\u003e \u003cp\u003eThe material used in this study is the A356 aluminium alloy (Al-Si-Mg), as shown in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, with the chemical composition as indicated in the following table:\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eChemical composition (in wt. %) of the A356 alloy used in this study\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"9\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSi\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMg\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFe\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eTi\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eCu\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eZn\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eSn\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eMn\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eAl\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e6.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.013\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.015\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eBalance\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe theoretical density of A356 is 2.685 g/cm\u0026sup3;. This alloy was chosen for its good fluidity, adequate corrosion resistance and reliable mechanical strength for light structural applications.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e2.3. Specimen and Mould Design\u003c/h2\u003e \u003cp\u003eTensile specimens are designed in accordance with JIS H5202 using 2D/3D design software. The geometric dimensions of the specimens strictly adhere to the provisions of this standard to ensure the reproducibility and comparability of mechanical test results. The channel system is designed using a dual-channel configuration with the aim of distributing the flow of molten metal evenly throughout the mould cavity and minimising turbulence at the gate.\u003c/p\u003e \u003cp\u003eThe negative mould is produced using 3D printing technology based on digital design geometry. This negative mould is then used as a template to create a positive gypsum cast, which subsequently serves as a reference in the final sand moulding process. The complete stages of the mould fabrication process include: 3D Negative Mould Printing \u0026rarr; Positive Gypsum Cast \u0026rarr; Mould Casting \u0026rarr; Finishing \u0026amp; Assembly Moulding. In Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, Material A356 was melted in a 500 kg capacity crucible furnace at approximately 675\u0026deg;C, with degassing times of 5, 7, and 10 seconds. After casting was complete, a visual inspection and finishing were performed to ensure the specimens were free of surface defects before entering the testing stage.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e2.4. The Smelting and Casting Process\u003c/h2\u003e \u003cp\u003eThe melting of the A356 alloy was carried out in a 500 kg crucible furnace at an operating temperature of 675\u0026deg;C. This temperature was selected on the basis that the liquidus point of A356 is around 615\u0026deg;C; thus, a temperature of 675\u0026deg;C provides sufficient superheat to maintain the fluidity of the molten metal during the mould filling process without causing excessive oxidation.\u003c/p\u003e \u003cp\u003eThe pouring parameters set as independent variables in this study are:\u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003ePouring temperature: 710\u0026deg;C (constant for pouring time variations) and variations in mould preheat temperature at 125\u0026deg;C, 150\u0026deg;C, and 200\u0026deg;C\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003ePouring time: 5 seconds, 7 seconds, and 10 seconds\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eMould preheat temperature: 125\u0026deg;C, 150\u0026deg;C, and 200\u0026deg;C\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e2.5. Degassing Process\u003c/h2\u003e \u003cp\u003eThe degassing process was carried out using a rotary impeller degassing unit with argon (Ar) as the purifying medium. Argon gas was passed through a rotating impeller which broke the gas bubbles down into fine particles, thereby increasing the contact area between the gas and the molten metal to efficiently extract dissolved hydrogen. The degassing times tested were 5 seconds, 7 seconds and 10 seconds per active purification cycle. The effectiveness of degassing was evaluated by means of a Density Index (DI) test in accordance with ASTM B962, with the density of the sample measured under two conditions: at atmospheric pressure \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\left({\\rho\\:}_{atm}\\right)\\)\u003c/span\u003e\u003c/span\u003e and under a vacuum of \u0026plusmn;\u0026thinsp;1 mbar (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\rho\\:}_{vac})\\)\u003c/span\u003e\u003c/span\u003e. The DI value is calculated using the formula:\u003cdiv id=\"Equa\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e\n$$\\:DI\\:\\left(\\%\\right)\\:=\\left[\\:\\right(\\frac{{\\rho\\:}_{atm}-{\\rho\\:}_{vac}}{{\\rho\\:}_{atm}}\\:]\\:\\times\\:\\:100$$\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Equb\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equb\" name=\"EquationSource\"\u003e\n$$\\:Reduksi\\:DI\\:\\left(\\%\\right)\\:=\\:\\left[\\:\\frac{({DI}_{Before}-{DI}_{After})}{{DI}_{Before}}\\:\\right]\\:\\times\\:\\:100$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e2.6. Numerical Simulation Settings\u003c/h2\u003e \u003cp\u003eThe casting simulation was performed using Altair Inspire Cast software. The simulation model was built based on the 3D design geometry of the specimen and the defined gating system. The simulation parameters entered included:\u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003eMaterial: A356 alloy with thermal and physical properties in accordance with the Altair Inspire Cast material database\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003ePouring temperature: As per the specified variation (710\u0026deg;C)\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eInitial mould temperature: 125\u0026deg;C, 150\u0026deg;C, and 200\u0026deg;C\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eFilling time: 5 seconds, 7 seconds, and 10 seconds\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003cp\u003eOutput parameters evaluated: Temperature distribution, mould filling pattern, solid fraction at the farthest flow point, freeze time, and prediction of potential defect-prone critical zones\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003e2.7. Mechanical Testing and Microstructural Characterisation\u003c/h2\u003e \u003cp\u003eFollowing the casting process, all specimens undergo visual inspection and surface finishing to ensure the absence of surface defects prior to entering the testing phase. The tests carried out include:\u003c/p\u003e \u003cp\u003e \u003col\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eTensile testing in accordance with ASTM E8/E8M, to determine the Ultimate Tensile Strength (UTS), Yield Strength (YS), and elongation (%)\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eMicrostructural characterisation in accordance with ASTM E3, to observe the morphology of α-Al dendrites, the distribution of eutectic silicon, and the distribution of gas porosity in the cross-section of the specimen\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eHardness testing in accordance with ASTM E10 (Brinell method)\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eDensity Index (DI) testing in accordance with ASTM B962, to quantitatively validate the effectiveness of the degassing process\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003c/ol\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"3. Results and Discussion","content":"\u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003e3.1. Model Design and Temperature Distribution Simulation\u003c/h2\u003e \u003cp\u003eThe initial stage of the research focused on designing a simulation model of the specimen and developing a simulation of the casting process as a basis for design optimisation. The model was designed taking into account time variations (5 seconds, 7 seconds and 10 seconds), the standard dimensions of the JIS H5202 specimen, the flow characteristics of molten metal, and the potential for defect formation during the solidification process. Simulations were carried out to predict temperature distribution, mould filling patterns, and the potential for porosity or turbulence. The results of this stage served as a reference for determining the most suitable process parameters prior to the actual casting.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe results of the temperature distribution simulation are shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e. The thermal map indicates that the maximum temperature reaches approximately 991.15 K (\u0026asymp;\u0026thinsp;718\u0026deg;C) in the sprue zone and the upper part of the feeding system, which are marked in red. These conditions confirm that the molten metal enters the system via the top with the highest thermal energy, consistent with the principle of gravity casting where potential energy is converted into the kinetic energy of the flow. Meanwhile, a minimum temperature of approximately 418.58 K (\u0026asymp;\u0026thinsp;145\u0026deg;C) was observed at the bottom and tip of the specimen, marked in blue, indicating the zone that cooled most rapidly due to the longest contact with the lower-temperature mould surface. The temperature gradient formed from top to bottom shows a relatively stable heat flow pattern, so that the solidification process tends to proceed gradually and in a directed manner.\u003c/p\u003e \u003cp\u003eThe even temperature distribution across the main section of the specimen, shown by the green to yellow colours on the thermal map, indicates that the proposed dual-channel design is capable of maintaining the continuity of the molten metal flow and reducing the risk of cold shut or misrun formation. More significantly, no significant isolated hot spots were found in the gauge length zone of the tensile specimen. The absence of hot spots in this critical area indicates that the risk of shrinkage porosity in the section directly determining the validity of the mechanical testing is low. Overall, the results of this stage demonstrate that the model design and simulation parameters established support a more controlled solidification process and are in line with the requirements of the JIS H5202 standard..\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003e3.2. Analysis of Simulation Results: The Effect of Pouring Time Variations\u003c/h2\u003e \u003cp\u003eOnce the validity of the channel design had been confirmed via the temperature distribution map, the analysis proceeded to a quantitative evaluation of the effect of pouring time variations on three key solidification parameters. The parameters evaluated included pouring temperature (\u0026deg;C), initial mould heating temperature (\u0026deg;C), pouring time (seconds), temperature at the farthest flow point (\u0026deg;C), solid fraction at the farthest flow point, freezing time (seconds), and the prediction of potential defect-prone critical zones. This analysis was conducted to assess the effectiveness of the channel and filling system design in producing a stable flow with minimal turbulence, whilst elucidating the relationship between the model design and the quality of the cast specimens produced.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eData from parameter simulation with pouring time variations\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eID\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCasting Temp (\u0026deg;C)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTemp Preheat (˚C)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePouring Time (sec)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eTemp at the farthest stream (\u0026deg;C)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eFarthest Flow Solid Fraction\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eFreeze Time (sec)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e710\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e150\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e580,95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0,47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e6,96\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e710\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e150\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e570,58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0,89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e7,31\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e710\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e150\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e557,21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003eVariable Control\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003eIndependent Variables\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003eBound Variables\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e shows the effect of variations in pouring time on the thermal conditions and solidification behaviour during the A356 casting process at a constant pouring temperature of 710\u0026deg;C and a mould preheating temperature of 150\u0026deg;C.\u003c/p\u003e \u003cdiv id=\"Sec19\" class=\"Section3\"\u003e \u003ch2\u003e3.2.1. The Effect of Pouring Time on the Temperature of the Far-End Flow\u003c/h2\u003e \u003cp\u003eThe data show a consistent downward trend in temperature at the farthest flow point as the pouring time increases. Under condition 1.01 (5 seconds), the temperature was still relatively high at 580.95\u0026deg;C; it decreased to 570.58\u0026deg;C at 7 seconds (condition 1.02); and reached a minimum of 557.21\u0026deg;C at 10 seconds (condition 1.03). The total decrease of \u0026plusmn;\u0026thinsp;23.74\u0026deg;C between the fastest and slowest conditions reflects the magnitude of heat loss due to prolonged contact between the molten metal and the mould surface and the surrounding environment during filling. Thermally, this condition increases the viscosity of the molten metal and reduces its fluidity, which has the potential to accelerate solidification at the flow front before the mould cavity is fully filled. The practical implication is that the longer the pouring time, the narrower the effective filling window becomes, thereby proportionally increasing the risk of misrun and cold shut.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section3\"\u003e \u003ch2\u003e3.2.2. Analysis of Solid Fractions and Defect Risk\u003c/h2\u003e \u003cp\u003eThe most dramatic and practically significant change was observed in the solid fraction value at the farthest flow point. This value jumped from 0.47 at a pouring time of 5 seconds to 0.89 at 7 seconds, and reached the perfect value of 1.00 at a pouring time of 10 seconds. The increase from 0.47 to 0.89 a rise of 89.4% achieved with just a 2-second extension in pouring time indicates that the critical fluidity threshold of the molten metal lies within the 5 to 7-second pouring window under the specified conditions.\u003c/p\u003e \u003cp\u003eA solid fraction value of 1.00 under condition 1.03 is a strong indicator of a misrun or cold shut, as the molten metal had completely solidified before the filling process was complete. The mould cavity could no longer be filled by sufficiently fluid metal. The absence of solidification time data under these conditions, marked as \"-\" in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, confirms that the system never reached normal solidification conditions because premature solidification disrupted the flow continuity before the system reached thermal equilibrium. From a metallurgical perspective, this sharp increase in the solid fraction also has the potential to produce a non-homogeneous microstructure due to an excessively steep temperature gradient, which in turn can significantly reduce the mechanical properties of the specimen.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec21\" class=\"Section3\"\u003e \u003ch2\u003e3.2.3. Freezing Time and Directed Solidification Stability\u003c/h2\u003e \u003cp\u003eThe freezing time increased from 6.96 seconds under condition 1.01 to 7.31 seconds under condition 1.02. This increase reflects a destabilisation of heat distribution, which further complicates the mechanism of directional solidification. At a pouring time of 7 seconds, heat is no longer distributed evenly, so solidification proceeds more slowly and is less directional compared to the 5-second condition, implying a greater potential for shrinkage porosity formation in the interdendritic zone due to the inability of the remaining molten metal to compensate for volume shrinkage. At 10 seconds, the solidification time was not recorded because the metal had solidified completely at the farthest flow point before the system reached normal solidification conditions, confirming that an excessively long pouring time causes significant heat loss and fundamentally disrupts the directional solidification mechanism.\u003c/p\u003e \u003cp\u003eBased on an integrated analysis of the three parameters, condition 1.01 (5-second pouring time) produced the most optimal solidification characteristics and was selected as the reference parameter for the subsequent experimental validation stage.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec22\" class=\"Section2\"\u003e \u003ch2\u003e3.3. The Effect of Variations in Mould Preheating Temperature on Flow Behaviour and Solidification\u003c/h2\u003e \u003cp\u003eMeanwhile, Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e shows the effect of variations in the mould preheating temperature on the thermal conditions and solidification characteristics during the A356 casting process. In this stage, the pouring temperature was maintained at a constant 710\u0026deg;C with a fixed pouring time of 7 seconds, so that changes in the system\u0026rsquo;s response could be analysed directly as a result of variations in preheat temperature: 125\u0026deg;C, 150\u0026deg;C, and 200\u0026deg;C.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eEffect of Preheat Temperature on Flow Behaviour and Solidification\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eID\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCasting Temp (\u0026deg;C)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTemp Preheat (˚C)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePouring Time (sec)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eTemp at the farthest stream (\u0026deg;C)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eFarthest Flow Solid Fraction\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eFreeze Time (sec)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e710\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e125\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e569,11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0,95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e710\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e150\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e572,45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0,85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e7,88\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e710\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e200\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e573,59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0,79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e8,29\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003eVariable Control\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003eIndependent Variables\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003eBound Variables\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cdiv id=\"Sec23\" class=\"Section3\"\u003e \u003ch2\u003e3.3.1. The Effect of Preheat Temperature on the Temperature of the Outmost Flow\u003c/h2\u003e \u003cp\u003eIn contrast to the trend observed in the pouring time variations, the results show an opposite trend: the temperature at the farthest flow point increases as the mould preheat temperature rises. Under condition 1.04 (preheat 125\u0026deg;C), the farthest temperature was recorded at 569.11\u0026deg;C, rising to 572.45\u0026deg;C at a preheat of 150\u0026deg;C (1.05), and reaching 573.59\u0026deg;C at a preheat of 200\u0026deg;C (1.06). This total increase of \u0026plusmn;\u0026thinsp;4.48\u0026deg;C reflects a reduction in the temperature gradient between the molten metal and the mould surface. Thermodynamically, a warmer mould surface slows the rate of heat transfer from the molten metal to the mould, allowing the metal to remain in its liquid phase for longer and maintaining its fluidity during the mould cavity filling process.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec24\" class=\"Section3\"\u003e \u003ch2\u003e3.3.2. Analysis of Solid Fractions and Flow Stability\u003c/h2\u003e \u003cp\u003eThe solid fraction shows a clear downward trend as the preheat temperature increases: from 0.95 at 125\u0026deg;C \u0026rarr; 0.85 at 150\u0026deg;C \u0026rarr; 0.79 at 200\u0026deg;C. A high solid fraction at low preheat temperatures indicates that the molten metal solidifies more rapidly before reaching the flow front a condition that can lead to misrun or cold shut due to significant heat loss during filling. Conversely, increasing the preheat temperature helps maintain the liquid phase for longer, resulting in a more stable flow distribution and reducing the risk of premature solidification.\u003c/p\u003e \u003cp\u003eA direct comparison between Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e and Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e reveals an important comparative finding: the solid fraction under condition 1.04 (preheat 125\u0026deg;C, pouring time 7 seconds) reached 0.95 nearly equivalent to full solidification despite having an identical pouring time to conditions 1.05 and 1.06. This demonstrates that the mould preheat temperature has an influence equivalent to variations in pouring time in determining the quality of cavity filling, and that the two variables interact synergistically in a way that cannot be evaluated separately.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec25\" class=\"Section3\"\u003e \u003ch2\u003e3.3.3. Freezing Time and Solidification Mechanisms\u003c/h2\u003e \u003cp\u003eAt a preheat temperature of 125\u0026deg;C (condition 1.04), the freezing time was not recorded because the system had approached full solidification before thermal equilibrium was reached; this is identical to the situation in condition 1.03, where variations in pouring time confirmed that an excessively low preheat temperature accelerates heat extraction and fundamentally disrupts the directed solidification mechanism. At preheat temperatures of 150\u0026deg;C and 200\u0026deg;C, the freezing time increased to 7.88 seconds and 8.29 seconds, respectively. This prolongation of the freezing time indicates that solidification proceeds more gradually, allowing for better shrinkage compensation and a more homogeneous microstructural distribution, as the residual molten metal has sufficient time to fill the interdendritic voids before the system reaches full solidification.\u003c/p\u003e \u003cp\u003eBased on a combined comparative analysis of Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e and Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, condition 1.06 (preheat 200\u0026deg;C, pouring time 7 seconds) provides the best solidification characteristics within the preheat variation group. Thus, the optimal simulation parameters identified are a pouring temperature of 710\u0026deg;C, a pouring time of 5 seconds, and a mould preheat temperature of 200\u0026deg;C.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec26\" class=\"Section2\"\u003e \u003ch2\u003e3.4. Mechanical Testing and Experimental Validation\u003c/h2\u003e \u003cp\u003eMechanical testing was carried out to evaluate the performance of A356 material through a series of mechanical tests and microstructural characterisation. These tests aim to verify the simulation results whilst assessing the influence of process parameters, particularly degassing treatment, on the mechanical properties and homogeneity of the material. A comprehensive approach was applied by combining tensile testing, microstructural analysis, and Density Index (DI) testing to provide a complete picture of the casting quality, both qualitatively and quantitatively.\u003c/p\u003e \u003cdiv id=\"Sec27\" class=\"Section3\"\u003e \u003ch2\u003e3.4.1. Procedures and Validation of the Degassing Process\u003c/h2\u003e \u003cp\u003eThe degassing process is carried out using a rotary impeller degassing unit with argon (Ar) gas as the purification medium at a flow rate of 1 L/min and a rotor speed of 600 rpm. The mechanism of operation involves the rotating rotor breaking the argon flow into fine bubbles of small diameter, thereby increasing the contact interface area between the gas and the molten metal to efficiently extract dissolved hydrogen prior to casting.\u003c/p\u003e \u003cp\u003eIt should be emphasised that the degassing times of 5, 7, and 10 seconds in this study refer to the active purification intervals of the rotary impeller per unit cycle within a controlled laboratory-scale degassing protocol, not to the total duration of degassing for the entire 500 kg melt. The total degassing session lasted approximately 22 seconds, which is consistent with aluminium casting industry practice for furnaces of that capacity. The authors acknowledge that the presentation in the original manuscript had the potential to lead to misinterpretation on this point, and the Methods section has been revised to prevent such ambiguity. The variations in active interval duration (5, 7, and 10 seconds) were designed to evaluate the effect of purification intensity per cycle on dissolved hydrogen content, which is reflected measurably through changes in the Density Index (DI) and microstructural morphology.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec28\" class=\"Section3\"\u003e \u003ch2\u003e3.4.2. Testing the Density Index (DI) as a Quantitative Validation of Degassing Effectiveness\u003c/h2\u003e \u003cp\u003eTo quantitatively validate the effectiveness of degassing, Density Index (DI) tests were carried out in accordance with ASTM B962 on all specimens under all conditions, both before and after degassing. The DI test results for three samples per condition for each degassing time variation are shown in Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e below:\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDensity Index (DI) Test Results for A356 Alloy Before and After Degassing\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"10\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eTime Degassing\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c5\" namest=\"c3\"\u003e \u003cp\u003eBefore degassing\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c8\" namest=\"c6\"\u003e \u003cp\u003eAfter degassing\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eDI reduction (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eQuality\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eρ\u003csub\u003eatm\u003c/sub\u003e (g/cm\u0026sup3;)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eρ\u003csub\u003evac\u003c/sub\u003e (g/cm\u0026sup3;)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eDI (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eρ\u003csub\u003eatm\u003c/sub\u003e (g/cm\u0026sup3;)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eρ\u003csub\u003evac\u003c/sub\u003e (g/cm\u0026sup3;)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eDI (%)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e5 sec\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.671\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.583\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.674\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2.634\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e54.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eAdequate\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.669\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.580\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.672\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2.631\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e54.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eAdequate\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.670\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.581\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.673\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2.633\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e55.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eAdequate\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAverage\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.670\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.581\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.673\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2.633\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e54.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e7 sec\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.671\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.583\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.675\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2.648\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e69.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eGood\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.670\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.581\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.674\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2.647\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e69.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eGood\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.672\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.584\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.675\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2.649\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e70.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eGood\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAverage\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.671\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.583\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.675\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2.648\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e69.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e10 sec\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.671\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.582\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.676\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2.657\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e78.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eGood\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.670\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.581\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.675\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2.656\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e78.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eGood\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.672\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.583\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.676\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2.658\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e79.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eGood\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAverage\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.671\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.582\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.676\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2.657\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e79.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003ePrior to degassing, the average DI values under the three conditions ranged from 3.29% to 3.33%, far exceeding the 2% threshold, confirming that the A356 melt contained dissolved hydrogen in high concentrations, which has the potential to form extensive gas porosity during solidification [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. The high DI values recorded for the initial A356 without degassing treatment ranged from 3\u0026ndash;5% under conventional melting conditions.\u003c/p\u003e \u003cp\u003eFollowing degassing treatment, the DI values decreased significantly and progressively in line with the duration of the active interval: 5-second degassing yields an average DI of 1.51% (54.5% reduction), 7-second degassing yields 1.00% (69.8% reduction), and 10-second degassing yields 0.70% (79.1% reduction). This trend quantitatively demonstrates that an increase in the duration of the active degassing interval is directly proportional to the effectiveness of hydrogen removal from the melt. The DI value following 10-second degassing (0.70%), which is well below the \u0026lsquo;good\u0026rsquo; threshold (\u0026lt;\u0026thinsp;1%), confirms the success of the purification process in producing a high-quality melt ready for casting.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec29\" class=\"Section2\"\u003e \u003ch2\u003e3.5. Tensile Test Results (ASTM E8/E8M)\u003c/h2\u003e \u003cp\u003eTensile testing was carried out on all six specimen conditions three before degassing and three after degassing at time intervals of 5, 7 and 10 seconds, in accordance with the ASTM E8/E8M standard. The parameters measured included Ultimate Tensile Strength (UTS), Yield Strength (YS) and elongation (%). The full results are presented in Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eTensile Test Results for A356 Alloy Before and After Degassing (ASTM E8/E8M)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"10\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eDegassing Time\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003eBefore degassing\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003eAfter degassing\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c10\" namest=\"c8\"\u003e \u003cp\u003eIncrease (%)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUTS (MPa)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eYS (MPa)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eEl. (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eUTS (MPa)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eYS (MPa)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eEl. (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eΔUTS\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eΔYS\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003eΔEl.\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e5 sec\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e162\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e121\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2,1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e198\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e152\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e3,8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e+\u0026thinsp;22,2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e+\u0026thinsp;25,6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e+\u0026thinsp;81,0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e158\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e118\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1,9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e201\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e155\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e3,9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e+\u0026thinsp;27,2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e+\u0026thinsp;31,4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e+\u0026thinsp;105,3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e160\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e119\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2,0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e199\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e153\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e3,7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e+\u0026thinsp;24,4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e+\u0026thinsp;28,6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e+\u0026thinsp;85,0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAverage\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e160,0\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e119,3\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e2,0\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e199,3\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e153,3\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e3,8\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e+\u0026thinsp;24,6\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003e+\u0026thinsp;28,5\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u003cb\u003e+\u0026thinsp;90,0\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e7 sec\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e157\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e117\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1,8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e218\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e168\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e5,1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e+\u0026thinsp;38,9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e+\u0026thinsp;43,6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e+\u0026thinsp;183,3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e155\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e115\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1,7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e221\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e170\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e5,3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e+\u0026thinsp;42,6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e+\u0026thinsp;47,8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e+\u0026thinsp;211,8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e159\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e118\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1,9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e220\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e169\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e5,2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e+\u0026thinsp;38,4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e+\u0026thinsp;43,2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e+\u0026thinsp;173,7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAverage\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e157,0\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e116,7\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e1,8\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e219,7\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e169,0\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e5,2\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e+\u0026thinsp;39,9\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003e+\u0026thinsp;44,8\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u003cb\u003e+\u0026thinsp;188,9\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e10 sec\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e153\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e113\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1,6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e241\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e185\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e6,8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e+\u0026thinsp;57,5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e+\u0026thinsp;63,7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e+\u0026thinsp;325,0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e151\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e112\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1,5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e244\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e187\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e7,0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e+\u0026thinsp;61,6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e+\u0026thinsp;66,9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e+\u0026thinsp;366,7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e154\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e114\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1,6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e243\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e186\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e6,9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e+\u0026thinsp;57,8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e+\u0026thinsp;63,2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e+\u0026thinsp;331,3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAverage\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e152,7\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e113,0\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e1,6\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e242,7\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e186,0\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e6,9\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e+\u0026thinsp;58,9\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003e+\u0026thinsp;64,6\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u003cb\u003e+\u0026thinsp;331,3\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec30\" class=\"Section2\"\u003e \u003ch2\u003e3.6. Condition Prior to Degassing \u0026ndash; Basic Mechanical Performance\u003c/h2\u003e \u003cp\u003ePrior to degassing treatment, the A356 specimens exhibited consistently low mechanical performance across all casting time variations. The average UTS ranged from 152.7 MPa (10 seconds) to 160.0 MPa (5 seconds), with an average YS between 113.0 MPa and 119.3 MPa, and very low elongation between 1.6% and 2.0%. The trend of decreasing UTS and YS with increasing pouring time is fully consistent with simulation predictions showing an increase in the solid fraction from 0.47 to 1.00 and a deterioration in solidification stability.\u003c/p\u003e \u003cp\u003eThe very low elongation (1.5\u0026ndash;2.1%) under pre-degassing conditions specifically confirms the detrimental effect of gas porosity and the acicular eutectic silicon morphology observed in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e. Needle-shaped eutectic silicon particles act as local stress concentrators that trigger early crack initiation under tensile loading, causing the specimen to fail at a strain far lower than its actual potential.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cdiv id=\"Sec31\" class=\"Section3\"\u003e \u003ch2\u003e3.6.1. Post-Degassing Condition \u0026ndash; Significant Improvement in Mechanical Performance\u003c/h2\u003e \u003cp\u003eFollowing the degassing treatment, all mechanical parameters increased significantly and showed a trend consistent with the duration of degassing. At 5 seconds of degassing, the average UTS increased to 199.3 MPa (+\u0026thinsp;24.6%), the YS to 153.3 MPa (+\u0026thinsp;28.5%), and the elongation to 3.8% (+\u0026thinsp;90.0%). Although substantial, these improvements remain limited, as the DI value following a 5-second degassing period remained at 1.51%, indicating incomplete hydrogen elimination.\u003c/p\u003e \u003cp\u003eAt a degassing time of 7 seconds, a further increase was clearly evident: the average tensile strength reached 219.7 MPa (+\u0026thinsp;39.9%), the yield strength 169.0 MPa (+\u0026thinsp;44.8%), and the elongation 5.2% (+\u0026thinsp;188.9%). This significant leap in improvement correlates directly with the reduction in the DI value to the 1.00% threshold, reflecting a significant reduction in gas porosity and the morphological transformation of the eutectic silicon from acicular to a more fibrous form, as observed in Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e. The particularly dramatic increase in elongation (+\u0026thinsp;188.9%) specifically indicates that the more rounded morphology of the eutectic silicon following degassing plays a major role in enhancing the material\u0026rsquo;s toughness.\u003c/p\u003e \u003cp\u003eThe best mechanical performance was achieved at 10 seconds of degassing, with an average UTS of 242.7 MPa (+\u0026thinsp;58.9%), YS of 186.0 MPa (+\u0026thinsp;64.6%), and elongation of 6.9% (+\u0026thinsp;331.3%). This UTS value of 242.7 MPa aligns with the optimal post-degassing performance range for A356 (220\u0026ndash;260 MPa) reported in the literature. This value also revises the initial claim in the manuscript abstract stating \"250 MPa\"; the actual, more precise value based on the 10-second degassing condition is 242.7 MPa, a figure that is scientifically verifiable.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec32\" class=\"Section3\"\u003e \u003ch2\u003e3.6.2. Comparative Analysis: Correlation between Tensile Data, DI, and Simulation Predictions\u003c/h2\u003e \u003cp\u003eThe correlation between tensile data, DI values, and simulated solid fraction reveals a highly coherent and mutually reinforcing pattern. The lower the DI value following degassing, the higher the UTS, YS, and elongation obtained, confirming that the dissolved hydrogen content (quantified via DI) is the primary controlling variable for the mechanical quality of the A356 specimens in this study. A 1% decrease in DI correlates with an average increase in UTS of approximately 21\u0026ndash;22 MPa\u0026mdash;a relationship that can serve as a basis for predicting quality in subsequent production batches.\u003c/p\u003e \u003cp\u003eOn the other hand, the influence of pouring time variations on mechanical properties, which was predicted to be significant by the simulation through changes in the solid fraction, was found to be effectively compensated for by adequate degassing. Specimens at a pouring time of 10 seconds (simulated solid fraction\u0026thinsp;=\u0026thinsp;1.00) actually yielded the highest UTS (242.7 MPa) after 10 seconds of degassing, proving that superior metallurgical quality of the melt is capable of overcoming the thermal limitations indicated by the simulation. This confirms that holistic optimisation, which simultaneously integrates pouring parameters, preheat temperature, and degassing treatment, is the most effective approach.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec33\" class=\"Section2\"\u003e \u003ch2\u003e3.7. Microstructural Characterisation: A Comparative Analysis Before and After Degassing\u003c/h2\u003e \u003cdiv id=\"Sec34\" class=\"Section3\"\u003e \u003ch2\u003e3.7.1. Microstructure Before Degassing \u0026ndash; Baseline Conditions\u003c/h2\u003e \u003cp\u003eMicrostructural observations of specimens without degassing treatment (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e) revealed metallurgical characteristics consistent with simulation predictions regarding defect risk. Across all pouring time variations, the α-Al matrix exhibited a coarse dendritic morphology with wide Secondary Dendrite Arm Spacing (SDAS), indicating a relatively slow cooling rate and solidification disruption due to the presence of hydrogen gas. The distribution of eutectic silicon in the interdendritic regions is evident in the elongated and sharp acicular morphology, which correlates negatively with the material\u0026rsquo;s toughness and elongation. The presence of significant gas porosity in the interdendritic regions was quantified via an average initial DI value of 3.30\u0026ndash;3.33% (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAt a pouring time of 5 seconds, the microstructure is relatively finer with smaller pores, consistent with the simulation prediction of the lowest solid fraction (0.47). At 7 seconds, the interdendritic size begins to increase, providing more space for gas accumulation. At 10 seconds, slower cooling caused a clear enlargement of the SDAS and a significant increase in pore size, fully consistent with the prediction of a solid fraction of 1.00 under simulation condition 1.03. These variations reinforce the conclusion that, without a gas-purification process, the microstructural quality of A356 tends to be non-homogeneous and may reduce the material\u0026rsquo;s mechanical performance.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec35\" class=\"Section3\"\u003e \u003ch2\u003e3.7.2. Microstructure After Degassing \u0026ndash; Improvement in Metallurgical Quality\u003c/h2\u003e \u003cp\u003eFollowing argon degassing treatment via a rotary impeller, microstructural observations (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e) revealed significant and consistent morphological changes across all degassing time variations. Firstly, the α-Al matrix exhibited a marked reduction in SDAS, reflecting more stable solidification due to reduced interference from gas bubbles. A smaller SDAS correlates positively with increased tensile strength and material hardness. Secondly, the morphology of the eutectic silicon underwent a transformation from a sharp acicular form to a smoother, more fragmented morphology (fibrous/globular), which directly contributes to increased toughness and elongation.\u003c/p\u003e \u003cp\u003eMost critically, there is a significant reduction in the number and size of gas pores throughout the post-degassing specimens, quantified by a decrease in the DI value from an average of 3.31% to 1.51% (5 seconds), 1.00% (7 seconds), and 0.70% (10 seconds). The previously dissolved hydrogen was successfully extracted by fine argon bubbles, resulting in a denser microstructure with a much lower level of internal discontinuities a condition that directly supports higher mechanical performance, as confirmed by the tensile data in Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec36\" class=\"Section3\"\u003e \u003ch2\u003e3.7.3. The Effect of Variations in Degassing Time on Microstructural Evolution\u003c/h2\u003e \u003cp\u003eVariations in degassing time (5 days, 7 days and 10 days) showed a consistent trend in microstructural changes. At 5 seconds of degassing, improvement was already clearly visible, though small residual pores were still present, consistent with a DI value of 1.51%. At 7 seconds of degassing, the porosity distribution was reduced more consistently, with the DI value precisely reaching 1.00%\u0026mdash;the critical transition point for degassing effectiveness. At 10 seconds of degassing, the microstructure displayed the best quality: the narrowest SDAS, the most homogeneous distribution of eutectic silicon, and the lowest porosity (DI 0.70%), resulting in a material with the highest structural integrity, confirmed by a UTS of 242.7 MPa in Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec37\" class=\"Section2\"\u003e \u003ch2\u003e3.8. Integrated Analysis: Correlation of Simulation, DI, Tensile Properties and Microstructure\u003c/h2\u003e \u003cp\u003eA synthesis of all simulation data (Tables\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e and \u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e), DI data (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e) and tensile data (Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e) enables a comprehensive comparative analysis, as summarised in Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e below.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSummary of Integrated Correlations: Simulation, DI, and Mechanical Properties\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePouring Time\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSolid Fraction (Sim.)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAt the start (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAt the end (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eFinal Mid-Term Exam (MPa)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eDI reduction (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eMicrostructural Quality After\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5 sec\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.47- controlled\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3,32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1,51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e199,3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e54,5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eQuite good - some small pores remain\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e7 sec\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.89- critical\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3,30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1,00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e219,7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e69,8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eFine - minimal pores\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e10 sec\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.00- critical\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3,33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0,70\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e242,7\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e79,1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eExcellent - virtually no pores\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThis integrated comparative analysis reveals significant findings: whilst numerical simulations identified a pouring time of 5 seconds as the safest condition from a misrun risk perspective (solid fraction 0.47), a degassing time of 10 seconds resulted in the most substantial improvements in metallurgical and mechanical properties (UTS 242.7 MPa, final DI 0.70%). This demonstrates that pouring parameters and degassing treatment must be optimised simultaneously; neither can substitute for the other. Numerical simulations effectively identify the safe limits of thermal parameters, whilst DI testing and tensile data provide experimental validation of actual metallurgical quality that cannot be predicted by thermal simulations alone.\u003c/p\u003e \u003cp\u003eThe most recommended parameter combination, based on the comprehensive analysis, is a pouring temperature of 710\u0026deg;C, a pouring time of 5 seconds, a mould preheat temperature of 200\u0026deg;C, and a degassing duration of 7\u0026ndash;10 seconds a combination that maximises solidification stability whilst minimising dissolved hydrogen content, resulting in A356 specimens with a homogeneous microstructure, minimal porosity, a tensile strength of up to 242.7 MPa, an elongation of up to 6.9%, and quality compliant with JIS H5202 standards.\u003c/p\u003e \u003c/div\u003e"},{"header":"4. Conclusion","content":"\u003cp\u003eBased on simulation and microstructural observations of A356 aluminium, it was concluded that the gating system design maintained a stable flow of molten metal and supported a gradual solidification process without forming significant hot spots in critical areas of the specimen. Variations in pouring time affect the decrease in the farthest flow temperature and the increase in the solid fraction; pouring times that are too long increase the risk of premature freezing and misruns or cold shuts. Meanwhile, increasing the mould preheat temperature has been shown to maintain the fluidity of the molten metal, reduce the solid fraction, and promote a more directed solidification mechanism.\u003c/p\u003e \u003cp\u003eIn terms of microstructure, the condition before degassing showed a coarse dendritic α-Al matrix with high gas porosity, while after degassing, the structure became finer, more homogeneous, and had lower porosity. Overall, the combination of pouring time, preheat temperature, and degassing process settings plays an important role in improving the microstructural quality, solidification stability, and potential mechanical performance of A356 aluminium.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e \u003ch2\u003eDeclaration of competing interest\u003c/h2\u003e \u003cp\u003eNo conflict of interest\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eFunding information\u003c/h2\u003e \u003cp\u003eNo Funding\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eJoni Arif: Conceptualisation, Methodology, Data Analysis, And Writing of Initial Draft Manuscripts.Mujiyono: Guidance, Review, and Editing of Manuscripts, ond Validation of Results.Tiwan: Laboratory Testing, ond Data Collection.Ardian Maulana: Simulation Using Altair Inspire Cast, Material Preparation.\u003c/p\u003e\u003ch2\u003eAcknowledgment\u003c/h2\u003e \u003cp\u003eThanks are also extended to Yogyakarta State University for providing the necessary facilities and support. In addition, the author is very grateful to his family and friends who have always encouraged him.\u003c/p\u003e\u003ch2\u003eData availability\u003c/h2\u003e \u003cp\u003eData will be made available on request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eTenali N, Ganesan G, Babu PR, AN INVESTIGATION ON THE MECHANICAL AND TRIBOLOGICAL PROPERTIES OF AN ULTRASONIC-ASSISTED, STIR CASTING Al-Cu-Mg MATRIX-BASED COMPOSITE REINFORCED WITH AGRO WASTE ASH PARTICLES (2024) Appl Eng Lett 9(1):46\u0026ndash;63. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.46793/aeletters.2024.9.1.5\u003c/span\u003e\u003cspan address=\"10.46793/aeletters.2024.9.1.5\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMilojević S, Stojanović B (2018) Determination of tribological properties of aluminum cylinder by application of Taguchi method and ANN-based model. J Brazilian Soc Mech Sci Eng 40(12). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1007/s40430-018-1495-8\u003c/span\u003e\u003cspan address=\"10.1007/s40430-018-1495-8\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKrstić J et al (2024) Application of Metal Matrix Nanocomposites in Engineering. Adv Eng Lett 3(4):180\u0026ndash;190. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.46793/adeletters.2024.3.4.5\u003c/span\u003e\u003cspan address=\"10.46793/adeletters.2024.3.4.5\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBrůna M, Galcik M, Pastircak R, Kantorikova E (2024) Effect of gating system design on the quality of aluminum alloy castings. Metals, 14(3).\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e.pdf.\u003c/span\u003e\u003cspan address=\"http://.pdf.\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSun Z, Hu H, Chen X (2008) Numerical optimization of gating system parameters for a magnesium alloy casting with multiple performance characteristics. J Mater Process Technol 199(1):256\u0026ndash;264. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.jmatprotec.2007.08.036\u003c/span\u003e\u003cspan address=\"10.1016/j.jmatprotec.2007.08.036\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eStefanescu DM (1998) Metals Handbook Volume 15 of the 9th Edition, vol. 15, p. 51\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJia H et al (2025) Refinement of A356 alloy using continuous rheological extrusion Al-Ti-V-B master alloy. China Foundry 22(2):222\u0026ndash;230. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1007/s41230-025-3135-0\u003c/span\u003e\u003cspan address=\"10.1007/s41230-025-3135-0\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDas P (2022) Effect of process variables and melt treatment conditions during cooling slope semi solid slurry generation process of Al-7Si-0.3 Mg alloy. Mater Today Commun 33. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.mtcomm.2022.104944\u003c/span\u003e\u003cspan address=\"10.1016/j.mtcomm.2022.104944\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eOthman AH, Saman AM, Abbas NM (2025) Yield Improvement for Sand Casting Product using Casting Simulation Tool. J Adv Res Appl Mech 134(1):90\u0026ndash;104. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.37934/aram.134.1.90104\u003c/span\u003e\u003cspan address=\"10.37934/aram.134.1.90104\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHarshwardhan L, Kaushik RC, Bheda T, Somasundaram M, NarendraKumar U (2023) In: Rajkumar K, Jayamani E, Ramkumar P (eds) Simulation of Sand-Casting Process Using Altair Inspire Cast BT - Recent Advances in Materials Technologies. Springer Nature Singapore, Eds., Singapore, pp 447\u0026ndash;456\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHardin RA, Beckermann C (2012) Integrated design of castings: Effect of porosity on mechanical performance. IOP Conf Ser Mater Sci Eng 33(1). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1088/1757-899X/33/1/012069\u003c/span\u003e\u003cspan address=\"10.1088/1757-899X/33/1/012069\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLiu J, Wang Q, Qi Y (2019) Atomistic simulation of the formation and fracture of oxide bifilms in cast aluminum. Acta Mater 164:673\u0026ndash;682. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.actamat.2018.11.008\u003c/span\u003e\u003cspan address=\"10.1016/j.actamat.2018.11.008\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCampbell J (2015) \u003cem\u003eComplete Casting Handbook\u003c/em\u003e. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/c2014-0-01548-1\u003c/span\u003e\u003cspan address=\"10.1016/c2014-0-01548-1\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLi T, Davies JMT, Luo D (2021) Consumption of Entrained Gases Within Bifilms During a Mg-Alloy Casting Process. Metall Mater Trans B Process Metall Mater Process Sci 52(5):3093\u0026ndash;3106. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1007/s11663-021-02237-z\u003c/span\u003e\u003cspan address=\"10.1007/s11663-021-02237-z\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLi T, Davies JMT, Zhu X (2021) Revealing the structure and evolution of entrained oxide film in Mg\u0026ndash;Y alloy castings. J Mater Sci 56(29):16671\u0026ndash;16685. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1007/s10853-021-06333-y\u003c/span\u003e\u003cspan address=\"10.1007/s10853-021-06333-y\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNajafi Y, Shabestari SG (2023) Investigation of the Effect of Inert Gas Bubbling Semi-Solid Process on the Microstructure and Mechanical Characteristics of AZ91 Magnesium Alloy. JOM 75(9):3924\u0026ndash;3934. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1007/s11837-023-06006-y\u003c/span\u003e\u003cspan address=\"10.1007/s11837-023-06006-y\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChen W-J, Lin C-X, Chen Y-T, Lin J-R (2016) Optimization design of a gating system for sand casting aluminium A356 using a Taguchi method and multi-objective culture-based QPSO algorithm, \u003cem\u003eAdv. Mech. Eng.\u003c/em\u003e, vol. 8, Apr. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1177/1687814016641293\u003c/span\u003e\u003cspan address=\"10.1177/1687814016641293\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDispinar D, Campbell J (2011) Porosity, hydrogen and bifilm content in Al alloy castings. Mater Sci Eng A 528:10\u0026ndash;11. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.msea.2011.01.084\u003c/span\u003e\u003cspan address=\"10.1016/j.msea.2011.01.084\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLiu S, Cao F, Zhao X, Jia Y, Ning Z, Sun J (2014) Characteristics of mold filling and entrainment of oxide film in low pressure casting of A356 alloy. Mater Sci Eng A. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.msea.2014.12.058\u003c/span\u003e\u003cspan address=\"10.1016/j.msea.2014.12.058\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhou E An Overview of A356 Aluminum Alloy \u0026ndash; Properties and Composition., Sanon Metal Tech. [Online]. Available: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.sanonchina.com/an-overview-of-a356-aluminum-alloy/?utm_source=chatgpt.com\u003c/span\u003e\u003cspan address=\"https://www.sanonchina.com/an-overview-of-a356-aluminum-alloy/?utm_source=chatgpt.com\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAssociation JS (1999) STD.JIS H 5202\u0026thinsp;\u0026ndash;\u0026thinsp;1999 Aluminium alloy castings.pdf. Japanese Stand Association\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCampbell J, Tiryakioğlu M (2022) Fatigue Failure in Engineered Components and How It Can Be Eliminated: Case Studies on the Influence of Bifilms. Met (Basel) 12(8):1\u0026ndash;18. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3390/met12081320\u003c/span\u003e\u003cspan address=\"10.3390/met12081320\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRaiszadeh R, Griffiths WD (2011) The effect of holding liquid aluminum alloys on oxide film content. Metall Mater Trans B Process Metall Mater Process Sci 42(1):133\u0026ndash;143. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1007/s11663-010-9439-4\u003c/span\u003e\u003cspan address=\"10.1007/s11663-010-9439-4\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eASTM E8 (2010) ASTM E8/E8M standard test methods for tension testing of metallic materials 1, \u003cem\u003eAnnu. B. ASTM Stand. 4\u003c/em\u003e, no. C, pp. 1\u0026ndash;27. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1520/E0008\u003c/span\u003e\u003cspan address=\"10.1520/E0008\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eArif J, Mujiyono M, Tiwan T (2025) Altair Inspire Cast Simulation Approach in Optimization of Casting Parameters of JIS H 5202 Tensile Test Specimens: Bridging the Gap between Solidification Theory and Experimental Validation, \u003cem\u003eFormosa J. Sci. Technol.\u003c/em\u003e, vol. 4, pp. 3195\u0026ndash;3212, Oct. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.55927/fjst.v4i10.271\u003c/span\u003e\u003cspan address=\"10.55927/fjst.v4i10.271\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eA. Engineering., Altair Engineering. Altair Inspire Cast \u0026ndash; Casting Simulation Software Overview. [Online]. Available: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://altair.com/inspire-cast#:~:text=From\u003c/span\u003e\u003cspan address=\"https://altair.com/inspire-cast#:~:text=From\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e product designers to foundry,giving users a competitive edge\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKim R-C, Hong K-R, Yang J-Y, Yang W-C (2024) High-pressure die casting process optimization for improving shrinkage porosity and air entrainment in carburetor housing with aluminum alloy using Taguchi-based ProCAST simulation and MADM-based overall quality index. Int J Adv Manuf Technol 132:893\u0026ndash;906. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1007/s00170-024-13428-8\u003c/span\u003e\u003cspan address=\"10.1007/s00170-024-13428-8\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJansson J, Olofsson J, Salomonsson K (2020) Simulation-driven product development of cast components with allowance for process-induced material behaviour. J Comput Des Eng 7(1):78\u0026ndash;85. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1093/JCDE/QWAA008\u003c/span\u003e\u003cspan address=\"10.1093/JCDE/QWAA008\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eOranga J, MIXED METHODS RESEARCH (Aug. 2025) MERITS, APPLICATIONS AND CHALLENGES. Int J Soc Sci 5(2):233\u0026ndash;238. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.53625/ijss.v5i2.11034\u003c/span\u003e\u003cspan address=\"10.53625/ijss.v5i2.11034\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":false,"email":"","identity":"journal-of-materials-science-materials-in-engineering","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"","title":"Journal of Materials Science: Materials in Engineering","twitterHandle":"","acdcEnabled":false,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"VoR Journals","inReviewEnabled":false,"inReviewRevisionsEnabled":false},"keywords":"Aluminum casting, Gating system, Numerical simulation, JIS H5202, Pouring parameters, Tensile specimen","lastPublishedDoi":"10.21203/rs.3.rs-9369548/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9369548/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"The quality of aluminium casting is greatly influenced by the gating system design and pouring parameters, but these two factors are often studied separately. This study aims to optimise the gating design and process parameters for A365 aluminium tensile specimens, in accordance with JIS H5202, using Altair Inspire Cast simulation. The approach used includes variations in pouring time, pouring temperature, mould preheat temperature, and degassing process to analyse temperature distribution, molten metal flow behaviour, and potential defects. Simulation results show a stable temperature gradient from the sprue to the end of the specimen, supporting gradual solidification without the formation of significant hot spots. Increased pouring time decreases the temperature of the farthest flow and increases the risk of misrun due to heat loss, while higher preheat temperatures maintain the fluidity of molten metal and reduce premature solidification. Microstructural analysis shows that before degassing, a coarse α-Al dendritic structure, acicular eutectic silicon, and high gas porosity are formed. After degassing, the microstructure becomes more homogeneous with finer dendrite arm spacing and a significant reduction in porosity. The integration of gating design, thermal parameters, and degassing has been proven to improve filling stability, reduce casting defects, and potentially increase mechanical performance by up to approximately 250 MPa. This research provides a practical approach to optimising the aluminium casting process to improve product quality and manufacturing efficiency.","manuscriptTitle":"Optimization of Casting Parameters and Gating Design for Aluminum Tensile Specimens According to Jis H5202 Using Numerical Simulation","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-04-21 12:18:24","doi":"10.21203/rs.3.rs-9369548/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"editorInvitedReview","content":"","date":"2026-05-04T08:15:27+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"203516248996774081920929565012492218668","date":"2026-04-20T10:20:22+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"266864889886519995761130145684018419703","date":"2026-04-16T18:36:03+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"100456418616784765059286850494381391653","date":"2026-04-16T12:11:53+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-04-14T11:58:34+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-04-13T07:11:40+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-04-13T07:11:29+00:00","index":"","fulltext":""},{"type":"submitted","content":"Journal of Materials Science: Materials in Engineering","date":"2026-04-09T13:42:19+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":false,"email":"","identity":"journal-of-materials-science-materials-in-engineering","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"","title":"Journal of Materials Science: Materials in Engineering","twitterHandle":"","acdcEnabled":false,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"VoR Journals","inReviewEnabled":false,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"5d89cb6c-bc41-4762-adf6-cf2d06bd8440","owner":[],"postedDate":"April 21st, 2026","published":true,"recentEditorialEvents":[{"type":"editorInvitedReview","content":"","date":"2026-05-04T08:15:27+00:00","index":19,"fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-04-21T12:18:24+00:00","versionOfRecord":[],"versionCreatedAt":"2026-04-21 12:18:24","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9369548","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9369548","identity":"rs-9369548","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}