Experimental Investigation and Parametric Optimization of Automated MIG welding Stainless Steel SS316 with Low Alloy Steel AISI 4140 on Mechanical Properties

Experimental Investigation and Parametric Optimization of Automated MIG welding Stainless Steel SS316 with Low Alloy Steel AISI 4140 on Mechanical Properties | 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 Experimental Investigation and Parametric Optimization of Automated MIG welding Stainless Steel SS316 with Low Alloy Steel AISI 4140 on Mechanical Properties Tajudin Ayub Deko, Habtamu Beri, Ali A. Rajhi, Alaauldeen A. Duhduh, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3854720/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract In marine, aerospace, automotive, and biomedical engineering, components and structures are made of different materials that have properties in order to meet specific performance requirements. Stainless steel 316 and low alloy steels are the most commonly used materials in the marine industry, where corrosive resistivity and high specific strength are the design requirements. This was the main importance of welding dissimilar metal using automated MIG to increase productivity, improve quality, reliability, and reduce labor costs. This research work aimed to study how different MIG process parameters (welding current, wire feed rate, gas flow rate, and welding speed) affect the mechanical properties of welding two different metals together, namely stainless steel 316 and low alloy steel 4140. To study the significance and contribution of each parameter, analysis of variance (ANOVA) were conducted using Minitab 21 software. The tensile strength and flexural strength experimental results showed that the welding current parameter has a high significant effect in both cases, followed by welding speed. The hardness result shows that the welding speed followed by the welding current has a significant effect on hardness. Weld metal had higher hardness than stainless steel and low alloy steel base metals. Automated MIG Welding Multi-Response Optimization Mechanical Properties ANOVA Flexural Strength Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Figure 10 Figure 11 Figure 12 Figure 13 Figure 14 Figure 15 Figure 16 Figure 17 Figure 18 1. Introduction Welding is a method of joining two similar or dissimilar metals or nonmetals using pressure or non-pressure. MIG welding produces little material waste and can be semi or fully automated. The MIG welding technique is ideal for joining materials during structure fabrication. The process can be used semi-automatically, automatically, or robotically; because of its ability to produce repeatable joints, the process has the potential to be used in mass production units. Automated MIG welding follows the same basic principle as manual or semi-automated MIG welding, which involves using a consumable wire electrode and a shielding gas to melt and join metals together at the weld joint[1–4]. The electrode wire's end is melted by the arc and then moved to the molten weld pool. It is crucial to note, however, that the actual temperature achieved by the arc will be determined by various parameters, including the welding current, voltage, and wire feed speed, as well as the kind and thickness of materials being welded. The research motivation behind this study is to explore the optimal parameters for automated MIG welding of dissimilar metals, particularly stainless-steel SS 316 and low alloy steel AISI 4140. [5–9] Achieving a reliable joint between two different metals with varying thermal properties can be challenging, and automated MIG welding is one solution to this problem. Poor weld quality and mechanical qualities, such as weak interfaces, porosity, and cracking, can arise from insufficient selection and optimization of automated Metal Inert Gas (MIG) welding process parameters.[10–16] The objective of the present research work is to weld SS316 stainless steel with AISI 4140 low alloy medium carbon steel by automated MIG and Welding stainless steel SS 316 with low alloy steel AISI 4140 presents difficulties owing to variations in chemical and physical properties, which can result in poor weld quality and mechanical qualities. This work intends to provide a dependable solution for welding these dissimilar metals and improve the weld quality and mechanical characteristics of the joints by performing experimental investigation and statistical analysis to optimize the MIG welding settings. 2. Literature Review There have been numerous studies regarding dissimilar metals welding by various method of welding. Some of these studies are presented in the following section. conducted a study on dissimilar weldments and found that the GTA welding method can weld with and without filler in AISI-4140 and AISI-316 combination of metals[17–19]. In this welding current has been found to increase as the wire feed is increased, with increasing voltage, the value of welding current decreases slightly. This factor is crucial in MIG welding because it affects how quickly droplets of metal cross the arc, which in turn defines the type of metal transfer. Each test sample is visually inspected for visual weld defects, weld bead reinforcement over base metal and weld metal penetration into the root of the weld groove. In one of the study focusing on optimizing welding process variables using MIG welding, the goal was to investigate the hardness of non-similar metal welding joints of Stainless steel 304 and C-25 steel by using 0.8 mm diameter stainless steel filler wire. [20–24] in this study GMAW was used to weld stainless steel AISI 304 and medium carbon steel 45C8. Yield strength, ultimate tensile strength, weld zone hardness, weld bead thickness, and welded joint reinforcement have all been reported. Welding current, welding voltage, gas flow rate, and welding speed are the factors used. The Taguchi Method was used for the experiment design, ANOVA and S/N ratio analysis was performed. To summarize, automated MIG welding is an efficient method for connecting different metals and steels. According to research, choosing the right factors, such as joint design, filler metal selection, welding settings, shielding gas composition, and heat treatment, may help assure high-quality welds[25–29]. There is a significant gap in information regarding the best parameters and process conditions for automated MIG welding of stainless steel SS316 with low alloy steel AISI4140. More research is required to determine the effect of variables such as heat input, filler metal selection, preheating method, and cooling rate on the mechanical properties and microstructure of the weld, and this knowledge gap can lead to significant improvements in the efficiency and reliability of the welding process, allowing industries to meet their demanding structural requirements. According to a review of these studies, a number of studies on austenitic stainless steels have been conducted [30–32]. The research concentrated on a specific application, different grades of material, and the optimization of a single welding parameter. While they have the same chemical composition and large applications, no such focus was given to SS316 and low-alloy steel AISI 4140 by considering the effect of wire feed rate and gas flow rate during welding and optimization of weld quality for dissimilar metals. 3. Materials and Methodology Following a review of the literature, proper base materials and filler material selection, controlling process parameters, research and methodology, electrode wire, and welding conditions are selected. The approach for this study comprises of the use of an automated Metal Inert Gas (MIG) welding procedure to combine stainless steel SS 316 with low alloy steel AISI 4140. Welding parameters, including current, welding speed, wire feed speed, and gas flow rate are changed within a set range [33–35]. Experimentation and statistical analysis are then used to evaluate the weld quality and mechanical qualities. Welding fixtures are utilized during the experiment to keep the materials in place and guarantee exact weld joint preparation. The resultant welds are then subjected to tensile strength, impact flexural bending test, and hardness testing to determine their appropriateness for practical applications[36]. The results of these tests are examined statistically to determine the best set of welding settings for producing high-quality welds with minimal distortion. The workpiece materials employed in this experiment were stainless steel (SS316) and low alloy steel (AISI4140). Type SS316 is austenitic chromium-nickel stainless steel with a molybdenum content of 2–3%. The presence of molybdenum improves corrosion resistance as well as strength at maximum temperatures. Furnace components, heat exchangers, jet engine parts, pharmaceutical equipment, valves, chemical equipment, tanks, evaporators, pulp, paper, and textile processing equipment are typical applications. Low alloy AISI 4140 steel is made up of chromium, molybdenum, and manganese. Due to its durability, high fatigue strength, abrasion and impact resistance, it has been widespread across many industrial sectors. Chromium and molybdenum are added to increase corrosion resistance. Molybdenum can be especially helpful when attempting to stave against corrosion brought on by chlorides. The chemical composition test of the base and weld metals was performed at the Ethiopian Quality and Standard Agency using SPECTRO Maxx LMX10 ARC/SPARK spectrometer features a single air optic, with CMOS sensors. It extends the relevant and applicable elemental wavelength range from 233 to 670 nm. Figure 1 below shows the tested samples under spectroscopy. Recognizing that carbon identification is of primary concern in all material functions during welding. Table 1 . Shown below is Chemical composition of studied base metals. Table 1 Chemical composition of studied base metals Materials Chemical composition (wt. %) Elements C Mn P S Si Cr Ni Mo N Fe Base SS316 0.0 1.9 0.04 0.03 0.70 18 12.0 2.40 0.10 65.1 Metals AISI4140 0.3 0.9 0.02 0.03 0.33 1.12 0.17 0.18 - 96.8 Table 2 . shown below is the Mechanical properties of SS316 and AISI4140, ER-309L stainless steel filler material with a diameter 1.2mm was employed as filler material. ER-309L basically it is an Austenitic stainless steel 309L is used to join stainless steel sheets from the 300 series, including 309, 309L, 304, 316, and others. The weld deposit is more resistant to inter-granular corrosion caused by carbide precipitation, which may happen if using straight 309 grade because of the lower carbon content. Table 2 Mechanical properties of SS316 and AISI4140 Mechanical properties Value Value SS 316 AISI 4140 Ultimate tensile strength Yield strength Percentage elongation (in 50mm) Rockwell hardness B Mean Coefficient of thermal expansion(0-649 o C) Thermal conductivity (@100 o C) 579MPa 290MPa 50% 79HRB (18.5) µm/m*K 22.3W/mK 655MPa 415MPa 25.70% 92HRB 12.2µm/m o C 42.6W/mK Shielding gas used pure argon it is most beneficial for maintaining the stability of an arc. Argon also helps with a narrower penetration, So it produces a cleaner weld. During the welding process, metals are exposed to temperatures around 700 0 C. Argon is used to protect the molten pool of metal against elements in an atmosphere. Depending on the material being welded and the choice of shielding gas, several welding voltage ranges are available for MIG welding. As a general rule, the MIG welding voltage range falls between 16 and 22 volts. Due to the fact that welding voltage was not chosen as a process parameter for optimization in this experiment, a best value welding voltage of 20V was chosen by taking into account the aforementioned criteria. The majority of industry professionals advise choosing an arc length for MIG welding that is between 9.5mm and 13mm, or about the diameter of the electrode being used, type and thickness of the material being welded, welding location, wire diameter and the feed rate are all critical aspects. Because MIG welding is automated in this experiment, it is easy to set the arc length, and 11mm is chosen as the arc length. Depending on the thickness of the material being welded, welding location, type of joint being utilized, the appropriate root gap size for a MIG welding joint may change. After taking those factors into account and doing a trial exercise, the optimal welding penetration was found to be 1.2mm. This root gap is chosen for this experiment. Depending on the thickness of the base metal and the welding position, the ideal welding gun or work angle must be chosen. The welding operator should hold the MIG welding gun at a 90-degree work angle when welding a butt joint, which is a 180-degree junction. The integrated automated MIG welding machines used in this experiment has a welding gun holder with a work angle arrangement that is simple to adjust to the necessary angle value. For this experiment, the welding gun angle is set at 90 0 to the base metals as shown in Fig. 2 below. 3.1 Experimental Set-up Experiments are being carried out at the Manufacturing Technology and Engineering Industry Research and Development Center at the welding training center workshop on the FIMER standard TM 350 MIG welding machine integrated with a welding gun fixture machine that facilitates the automatic MIG welding process, as shown in the Fig. 3 . Automatic MIG welding is a process that can be used to control welding speed and achieve more consistent welds. The weld joint is positioned by a human operator, but the MIG gun is often mounted on a welding gun fixture or welding carriage that is controlled by a control panel or adjustment board. The control panel knobs can be used to set the exact speed at which the electrode wire should be fed, ensuring that every weld is completed at the same rate. Automatic MIG welding can also include additional features such as adaptive control, which monitors and adjusts the weld in real-time as conditions change. In this experiment, an automatic welding mechanism is used because semi-automatic welding with MIG welding equipment has limitations. The gun moves at an assigned rate during automatic operation, which helps to regulate and establish the welding speed. When the arc length changes, the operation's voltage and current both must change at the same time. Controlling the arc length and welding speed is necessary for better continuous welding quality as well as for the operator to select the welding parameters, such as welding current and voltage, in accordance with the Taguchi design orthogonal array. In order to take welding speed into consideration as one of the welding parameters, adopting automatic MIG welding was ultimately more beneficial than manual MIG welding. Welding fixtures are utilized in this experiment to help manage the root gap of welding and eventually improve the weld quality. This is crucial since the root gap may have an impact on the weld's penetration and strength. Welding fixtures are made to accommodate particular sizes and shapes of workpieces. This guarantees that each workpiece will keep a constant distance and the final weld will be of higher quality and have more penetration. 3.2 Preparation of Samples and Experimental Condition Welded samples are shown in Fig. 4 , stainless and low alloy steel sheets with dimensions of 100mm×75mm×3mm were made with a shear cutting machine called E21S ESTUN. Before welding, the edges of the work components were suitably prepped. According to Taguchi's Design of Experiments, the L9 orthogonal array was used to determine process parameters such as welding current, wire feed rate, gas flow rate, and welding speed at three different levels. Table 3 , for the experiment is displayed beneath the L9 orthogonal array. The welding process parameters and their levels are chosen according to the general approach which entails conducting a review of the literature and trial exercises to establish the best parameters based on elements such material thickness, joint geometry, and desired weld quality. Table 3 Selected process factors and their levels S. No Process factors Units Levels 1 2 3 1 Welding current Amp (A) 110 125 140 2 Wire feed rate cm/s 7 10.5 14 3 Gas flow rate CFH 7 9 11 4 Welding speed cm/min 20 30 40 3.3 Weld Bead Width Geometry The geometry of the weld bead is the primary determinant of MIG weld quality. Parameters of the welding process, such as welding current, shielding gas flow rate, welding speed, wire feed rate, and gap distance have a significant impact on bead geometry. Important factors in determining the mechanical features of the weld include the penetration and area of penetration, the heat-affected zone, the bead width, height, and penetration, in addition to the mechanical properties like strength of tensile, hardness, and bending strength. Each welded sample's width was measured using the 0.01 mm count method to determine the bead width. Comparable calipers. The ideal weld bead size for a given application can vary depending on a number of variables, including the base metal's thickness, composition, joint design and the intended use of the welded product. Due to the fact that each welded sample's width varied depending on the factors employed, the weld with the smallest bead width was taken as to be a better weld because it produced a reduced heat-affected zone when there was sufficient welding penetration. Bead width of the weld is illustrated in Fig. 5 . 3.4 Welding defects Acceptance Criteria The acceptability criteria for defects in welding such as porosity, cracks, absence of fusion, slag, and excessive reinforcement are described in AWS D1.1/D1.1M:2020 and ASME Section VIII []. These criteria are based on elements such as the size, position, and direction of the faults. The maximum allowed lengths for each type of fault are as follows: According to AWS D1.1/D1.1M:2020, cracks should not exceed 3mm (1/8 in), porosity should not exceed 5% of total area in a 100 mm (4 in) length of weld or 1% in a 25 mm (1 in) length of weld, lack of fusion should not exceed 3mm (1/8 in) in a single pass weld, lack of fusion should not exceed 3mm (1/8 in) in a single pass weld, slag inclusions should not exceed a depth of 3mm (1/8 in) or width of 6mm (1/4 in), and Excessive reinforcement should not be more than three times the thickness of the base metal or 19mm (3/4 in), whichever is less. ASME Section VIII also specifies defects limitations, which may differ somewhat from those defined in AWS D1.1/D1.1M:2020 depending on the application and needs (American National Standard Structural Welding, 2020). The acceptance criteria for surface defects in welding that allows a maximum percentage of defects of less than 5% is typically specified in welding standards and codes such as ISO 5817:2014, AWS D1.1/D1.1M:2020, and ASME Section VIII. The percentage of surface are calculated by the ratio of the sum of the area of surface defects to the total surface area of welded zone (weld bead). POSD = \(\frac{\sum Ad}{Aw}\) x100%, Where Ad = area of each surface defects and Aw = surface area of weld zone 3.5 Welding Defects Using Non-Destructive Testing (NDT) Manufacturing, construction, and other industries that rely on welded connections frequently experience welding defects. These defects may jeopardize the joint's structural integrity and cause failures, which might be harmful in applications like pipelines, pressure vessels, ships, and bridges. Non-destructive testing (NDT) techniques offer a way to find defects in welding without causing damage to the welded connection, enabling prompt diagnosis and remedial action. In this study this study investigated the application of NDT techniques, particularly liquid penetrant testing (LPT) and liquid fluorescent testing (LFT), to detect and classify welding flaws. The many types of welding flaws, their causes, and the factors influencing their occurrence were also covered. After the test is operated and the type of defects are identified by the visual inspection method, and then the length of these defects is measured as shown in Fig. 6 below for further analysis. 3.6 Liquid Penetrant Test (LPT) Testing with Liquid Penetrants (LPT) is a non-destructive testing method used to locate surface-breaking discontinuities in a wide range of materials, including metals, polymers, and ceramics. It entails putting a liquid penetrant to the surface of the material, letting it to seep into small gaps and fractures, and then removing the surplus penetrant before applying a developer that pulls the penetrant out of the surface. LPT has several applications in areas such as aerospace, automotive, and manufacturing. It is especially effective for identifying surface cracks, porosity, laps, seams, and other defects that might compromise the integrity of a material or component. During manufacturing or maintenance, LPT is commonly used to examine welds, castings, forgings, and machined components. The process of conducting an LPT typically involves several steps. The penetrant is applied over the entire surface using a spray bottle, brush, or dipping. The next step is applying a dry cloth to remove any excess penetrant from the surface. This process also aids in the removal of any non-penetrated liquid on the surface, which might cause misleading signals during application. Then, the penetrant begins to migrate out, revealing defects wherever there are traces of penetrant. Finally, faults discovered are reported and the part can be evaluated further. 3.7 Liquid Fluorescent Test (LFT) The Liquid Fluorescent Test (LFT) is a sophisticated Non-Destructive Testing (NDT) method that is widely used to detect flaws in metals. It works on the same principles as LPT, with a liquid penetrant applied to the material's surface. LFT, on the other hand, employs a fluorescent penetrant that fluoresces under UV light, allowing it to identify extremely tiny defects and crack discontinuities. The procedure for doing an LFT begins with surface preparation, which includes eliminating any residues of oil or grease that may function as barriers to the penetrant. The surface is then sprayed or coated with a luminous liquid penetrant. A black light illuminator is used to highlight any fluorescing indications on the surface under proper lighting circumstances. This aids in identifying possible cracks and indicators, which may be documented and investigated further. All defects observed are shown in Figs. 7 . 3.8. Tensile Strength MIG-welded experimental specimens made from SS316 and AISI4140 were prepared for various mechanical testing and machined in accordance with ASTM to establish the requisite dimensions for examining their properties. For conducting Tensile test Universal testing machine, a max force of 50 KN; at Adama Science and Technology University. Figure 8 a–d demonstrates how the specimen was made, and how the specimens were broken after the tensile test. 3.9. Hardness Testing Using calibrated hardness-testing equipment, the hardness of a welded specimen is determined by pressing a hardened steel ball or diamond point into its flat surface while applying a predefined load. The size of the indentation that occurs is then measured. Hardness testing was carried out at the material engineering laboratory of Adama Science and Technology University utilizing a Vickers hardness testing equipment. The resulting impression was measured and analyzed under a microscope before being converted into a hardness number. The samples were sectioned for hardness testing using an abrasive cutter as shown in the Fig. 9 . 3.10 Flexural Testing The maximum stress tolerance of a material is measured by its flexural strength, which is a force (measured in newton) per unit area. Flexural test performed at Adama Science and Technology University. Follow these procedures to run this test: first, to get accurate findings, cut a rectangular bar-shaped sample out of the material, making sure that the length is at least four times the breadth. The samples dimensions with 3mm x 18mm x 130mm are prepared as shown in Fig. 10 (a-d) given below. Second, position the sample on two lower supports with the standard-recommended distance (L) between them. Utilize two top loading points that are more closely spaced apart than the bottom supports to apply the weight. Third, until the sample cracks or gets the specified deflection, apply a load at a steady pace. Keep track of the load and deflection readings during the test. Fourth, use the following formula to get the material's flexural strength: Flexural strength is calculated as The 4-point test formula for determining a rectangular specimen's flexural strength is: σ = 3LF/(2bd 2 ) (1) in which b = specimen width, d = specimen thickness, L = specimen length, and F = total force applied to the specimen by two loading pins. Fifth, use the following calculation to get the stress at maximum load: Stress is calculated as (M*c)/ (I*y), where M is the maximum bending moment, c is the vertical distance in either compression or tension from the neutral axis to the extreme fiber, I is the moment of inertia of the sample's cross-section, and y is the perpendicular distance from the centroid axis to the extreme fiber. In materials science and engineering, the four-point bending test is frequently employed to evaluate the strength and stiffness of many materials, including plastics, composites, ceramics, and metals. 4. Taguchi Orthogonal Array In this experiment, the welding current, wire feed rate, gas flow rate, and welding speed were all investigated. The total degrees of freedom of the process parameters in the Taguchi DOE concept determines the utilization of an orthogonal array. The degree of freedom is accounted for or defined as the quantity of comparisons necessary to maximize each parameter (factor) with the chosen levels. Numerical analysis and experimental design of process parameters are created using Minitab 21 statistical software with four elements and three levels of the experiment. Three levels of study were conducted to examine the effects of welding parameters on tensile strength, hardness, flexural strength, and weld bead width while MIG welding dissimilar 316 stainless steel with AISI 4140 low alloy steel. The total number of degrees of freedom provided must be greater than the number of experiments required to look into the variables. In this study, the choice of orthogonal array was determined by counting the layers of different factors. Three levels were selected for each of the four factors in the experiments. DOF = P*(L-1), in which DOF = Degree of freedom, P = Number of factors and, L = Number of levels, DOF = 4(3 − 1) = 8 (OA > DOF, OA has 9 experimental runs). The L9 orthogonal array was chosen for the experiment design because the total number of orthogonal array (OA) experiments should be more than or equal to the computed value. 4.1 Signal to noise ratio (S/N ratio) The Taguchi approach highlights the importance of analyzing response variation using the signal-to-noise ratio because it lessens the impact of uncontrollable parameter fluctuation on quality features. In order to evaluate the quality characteristic that deviates from the ideal value, Taguchi employed the S/N ratio, which is equal to the average to SD ratio. Depending on the type of feature, there are a variety of S/N ratios accessible; smaller is preferable, nominal is preferable, and bigger is preferable. Larger the better (Maximization), Smaller the better (Minimization), Nominal the best. Standard S/N ratio formula is given by \(\frac{S}{N}\) Ratio = -10 \(\left[\frac{1}{n} \sum _{j=1}^{n}\frac{1}{{Y}_{{ij}^{2}}}\right]\) , Where 'i' is the number of a trial; 'Yi' is the measured value of quality characteristic for the ith trial and jth experiment, 'n' is the number of repetitions for the experimental combination. 4.2 Analysis of Variance (ANOVA) ANOVA is a statistical approach for determining the impact of numerous control conditions. The percentage contributions of each control factor are utilized in this research to quantify their influence on performance attributes. This analysis took a 5% significance level (or a 95% level of confidence) into account. A significance level of less than 0.05 is deemed significant in ANOVA analysis, whereas a value greater than 0.05 is not. In the analysis of variance, the percentage contribution of each influential parameter was computed using the following formula Percentage contribution of individual factor, SS: Sum of squares or treatment sum of squares, SST: Total sum of squares. 5. Results and Discussion The effective of welding aspects on the mechanical characteristics of the non-similar weld of SS-316 stainless steel with AISI-4140 low alloy steels was determined. the welded samples were visually inspected for welding defects using liquid penetrant. 5.1 Liquid Penetrant and Visual Defect Testing All samples are analyzed in terms of the type of defects and their length by calculating the surface area of each defect on the weld zone divided by total surface area of weld zone to get the percentage of defects on the surface. According to AWS D1.1/D1.1M:2020, and ASME Section VIII, the acceptance criteria for surface defects in welding that allows a maximum percentage of defects of less than 5% is typically specified in welding standards. These standards define the acceptable levels of various types of welding defects, such as porosity, undercutting, and small crack. The acceptance criteria for welding flaws such as cracks, porosity, lack of fusion, slag, and excessive reinforcement are defined in AWS D1.1/D1.1M:2020 and ASME Section VIII based on their size, location, and orientation. The maximum allowable defect lengths for each category are as follows: - (a)Small cracks Samples 2, 3, 4 and 6 exhibited a small crack. This defect appeared as a result of slag inclusion due to the welding speed is too fast. AWS D1.1/D1.1M:2020 specifies that If the crack is due to slag inclusion then the maximum length will be around 1/8 in (3 mm). Therefore, all samples with small crack are not exceed 3mm and accepted. (b)Porosity Samples 1 and 8 exhibited a porosity. This defect was caused by trapped air in the shielding gas, which caused distributed porosity and gross surface pore breaking. According to AWS D1.1/D1.1M:2020 and ASME Section VIII, the maximum diameter of porosity in welds should not exceed 3/32 inches (2.4 mm) for most applications. Therefore sample 1 and 8 have porosity diameter of 0.8mm and 1mm respectively they are accepted. (c)Lack of fusion Samples 3, 4 and 5 exhibited a lack of fusion. This defect appeared as a result of low current input, electrode angle, arc length, electrode manipulation, and incorrect welding parameter settings, according to AWS D1.1/D1.1M:2020 all lack of fusion defects are accepted except lack of fusion in sample 3 which has 6.4mm in length. (d)Excessive reinforcement Samples 2 and 3 exhibited an excessive reinforcement. This defect was caused at lower welding current and higher wire feed rate. Therefore, they are accepted. Sample 3 having the highest POSD due to the lower welding current, high wire feed rate, and high feed rate. The percentage of surface defect has been calculated for sample 3 using Fig. 11 of the report of all samples obtained from the liquid penetrant testing laboratory center at the Manufacturing Technology and Engineering Industry Research and Development Center, Ethiopia. (e)Micro-hardness Analysis On both sides of sample 3 and sample 7, the differences in Vickers micro-hardness values are shown in Fig. 12 according to the separation between the base metals and the weld center. The hardness values of all the weld regions were considerably higher than the base metals' hardness. The area that is welded is the hardest. 5.2 Mechanical Properties Investigating and improving automated MIG welding process parameters for welding SS 316 and AISI 4140 with dissimilar metals was the aim of this study. It is accomplished by inspecting the mechanical attributes (tensile, hardness, flexural bending attributes. Following that, the Taguchi method was used to examine the test response parameters, and analysis of variance (ANOVA) was used to calculate the percentage contribution of each parameter. 5.2.1 Tensile Strength Result Analysis A universal testing equipment was used to acquire tensile strength data. Each test included two specimens, with the average value being taken. Table 7 shows the measured experimental results of the mean tensile strength values for 15each trial experiment. The failure of the testing sample following the test on the stainless steel and in sample 2 and sample 3 failure occur at weld zone which shows the tensile strength of low alloy steel is better than stainless steel. Figures 13 and 14 below shows the stress strain graph for maximum and minimum tensile strength appeared in sample 7 and sample 3 respectively. As indicated in Table 4 , the most affected parameters were level three welding current (A), which was followed by welding speed (D) at level one, wire feed rate (B) at level one, and gas flow rate (C) at level one. Taguchi utilized these welding factors (A3B1C1D1) as the first parametric configuration for the stainless steel 316 MIG welding to low alloy steel 4140 with a thickness of 3 mm. This implies that it is possible to employ that as a parameter combination in order to generate larger and better outcomes. Table 4 Response table for Means of Tensile Strength Level Welding Current(A) Wire feed rate (B) Gas flow rate (C) Welding speed (D) 1 568 617 608 618 2 603.3 598.7 603 603.3 3 626.7 582.3 587 576.7 Delta 58.7 34.7 21 41.3 Rank 1 3 4 2 The numerical value at the maximum point in each graph of the main effect plot shown above Fig. 17 denotes the factor's optimal value at indicated level. The optimal parameters (A3B1C1D1) were welding current at level three (140A), wire feed rate at level one (7cm/s), gas flow rate at level one (7 CFH), and welding speed at level one (20cm/min). According to the signal-to-noise ratio value in Table 9, the welding current weight percentage has the greatest influence, (Delta = 0.87; Rank = 1st) followed by welding speed (Delta = 0.63; Rank = 2nd) and wire feed rate (Delta = 0.53; Rank = 3rd). The (S/N) ratio is least affected by gas flow rate (Delta = 0.34; Rank = 4th) according to the experimental result. The initial best parameter combination for increasing weld metal strength with the greater-the-better responsiveness of automated MIG-welded of 316 stainless steel sheets with 4140 low alloy steel is A3B1C1D1. Figure 18 shows how these variables affect tensile strength. According to the graph when the welding current rises from level 1(one) to level 3(three), the strength of tensile increases significantly. The tensile strength value decreases from level one to level three as the wire feed rate rises, as well as the tensile strength value decrease from level one to level three when gas flow rate and welding speed rises observed the same outcome. 5.2.2 Analysis of Variance (ANOVA) for tensile strength ANOVA was used to assess the appropriateness of the created models. The model is stated to be adequate within the confidence limit if the estimated value of the F-ratio of the produced model is less than the standard F-ratio (F0.05, 1, 8 = 5.32 from analysis ) value at the required level of confidence of 95%. Table 10 shows how analysis of variance (ANOVA) is used to identify the most important characteristics that affect the tensile strength of MIG-welded steels. All parameters have a significant impact on tensile strength because the F-value from the table is 4.7 and the F-ratio from the ANOVA table is significantly higher than the critical F-ratio. As illustrated in the Fig. 15 , the weight percentage of welding current (49.66%), Welding speed (24.65%), Wire feed rate (17.34%), and Gas flow rate (6.36%) all have a significant effect on the tensile strength of automated MIG-welded 316 stainless steel with 4140 low alloy steel sheets with a thickness of 3 mm, as indicated in the table above. The ANOVA analysis, including p-values, indicates that the percentage of contribution from all parameters significantly impacts the tensile strength as shown in Fig. 15 , (P < 0.05). The R-squared value, which measures the model's fit to the data, is 98.01% according to table 10, indicating a strong fit. The errors in statistical models are believed to be regularly distributed in the design of experiments. A normal probability plot of the residuals is used to evaluate the importance of effects. The residuals' normal probability plots reveal that they are pretty near to the normal probability lines. Furthermore, the p-value (0.052) is larger than 0.05, indicating that the residuals are normally distributed and that the variables in the regression models are both significant and appropriate. 5.3 Hardness Test Results Analysis Hardness measurements were taken with a Vickers hardness test machine, every test being run on the weld zone, HAZ, and base metal. Each zone had three readings, and the average value was computed. The Fig. 16 below show when Vickers hardness test machine display first trial of sample 7 which is 541HV. Table 5 displays the average hardness values for each trial experiment as determined by the measured experimental results. For superior performance qualities, the larger hardness was predicted to be the preferred alternatives. Table 5 Mean hardness testing result Sample No Welding Current (A) Wire feed rate (B) Gas flow rate (C) Welding speed (D) Hardness (HV) Signal to noise ratio 1 110 7 7 20 482 53.6609 2 110 10.5 9 30 435 52.7698 3 110 14 11 40 456 53.1793 4 125 7 9 40 599 55.5485 5 125 10.5 11 20 435 52.7698 6 125 14 7 30 482 53.6609 7 140 7 11 30 541 54.6639 8 140 10.5 7 40 625 55.9176 9 140 14 9 20 476 53.5521 The welding parameters are slected such as welding current (A) at level 3, wire feed rate (B) at 1st level, gas flow rate (C) at 1st level, and welding speed (D) at 3rd level have the greatest value of hardness. According to the experimental response data, these welding settings (A3B1C1D3) were selected as the initial parametric hardness configuration when automated MIG-welding AISI 4140 low alloy steel with a 3 mm thickness to stainless steel 316. Because the response is larger the better, the major influence plot for control parameters for mean hardness value is essentially the same as the main effects plot for SN ratios, therefore the impacts of the factors on hardness are evaluated from the main effect plot for SN ratios.The welding speed weight percentage had the biggest impact (Delta = 1.55; Rank = 1st), followed by welding current (Delta = 1.51; Rank = 2nd), and wire feed rate (Delta = 1.16; Rank = 3rd), as shown by the value of the ratio of signal to noise in Table 6 . The gas flow rate had the least impact on the S/N ratio (Delta = 0.88; Rank = 4th). The Taguchi initial optimal parameter combination for raising the hardness depends on the larger-the-better characteristic of dissimilar automated MIG-welded SS316 stainless steel and AISI 4140 low alloy steel is A3B1C1D3. Table 6 Response table for signal-to-noise ratios for Hardness Level Welding Current (A) Wire Feed Rate (B) Gas flow Rate (C) Welding Speed (D) 1 53.2 54.62 54.41 53.33 2 53.99 53.82 53.96 53.7 3 54.71 53.46 53.54 54.88 Delta 1.51 1.16 0.88 1.55 Rank 2 3 4 1 The main impact plot graph and analysis for the S/N ratios indicate that level 3 current is the best value for maximizing hardness. (140A), wire feed rate at level 1 (7 cm/s), gas flow rate at level 1 (7 CFH), and welding speed at level 3 (40 cm/min). As the current rises from level 1(one) to level 3(three), the hardness value is rises. The hardness of the material decreases as the wire feed rate increases from level 1(one) to 3(three). As the gas flow rate increased from level 1(one) to 3(three), the hardness value is decreased. The welding speed was the most important welding factor, when welding speed rises from level one to level three the hardness value is increased discovered the same result for weld in AISI 4140 Steel. 5.3.1 Analysis of Variance (ANOVA) for hardness The ANOVA was developed for hardness to investigate the developed experiment was adequate or not. The most important qualities (most important parameters) that affect the automated MIG-welded steels' hardness are found and showed in Table 14 using analysis of variance (ANOVA). Research was undertaken utilizing their S/N ratios to establish the percentage contribution of each component. All of the factors had a very significant impact on hardness because the F-value in the ANOVA table 14 was more than the F-critical. Hardness of automated MIG-welded SS 316 stainless-steel with AISI 4140 low alloy steel with a thickness of 3 mm was significantly affected by the welding speed percentage (35.56%), welding current (31.24%), wire feed rate (18.68%), and gas flow rate (10.64%) as shown in Fig. 17 below is the Percentage contribution of parameters for hardness. The ANOVA analysis, including p-values, indicates that the percentage of contribution from all parameters significantly impacts the hardness (P < 0.05). The R-squared value, which measures the model's fit to the data, is 96.11% indicating a strong fit. The errors in statistical models are believed to be regularly distributed in the design of experiments. A normal probability plot of the residuals is used to evaluate the importance of effects. If the residuals fall along a straight line on the plot, they have a normal distribution. The data points are pretty near to the fitted normal distribution line. The residuals' normal probability plots reveal that they are pretty near to the normal probability lines. Furthermore, the p-value (0.105) is larger than 0.05, indicating that the residuals are normally distributed and that the variables in the regression models are both significant and appropriate. The maximum bending force was obtained via a four -point bend test performed on a universal testing machine, and flexural strength was calculated using this value and cross-section area. Table 7 displays the mean flexural strength values measured for each trial experiment. Table 7 Flexural strength test result No Weld Current (A) Wire feed Rate (B) Gas flow Rate Weld speed (D) Flexural strength (MPa) Signal to noise ratio 1 110 7 7 20 788 57.9305 2 110 10.5 9 30 556 54.9015 3 110 14 11 40 455 53.1602 4 125 7 9 40 698 56.8771 5 125 10.5 11 20 766 57.6845 6 125 14 7 30 727 57.2307 7 140 7 11 30 831 58.3920 8 140 10.5 7 40 756 57.5704 9 140 14 9 20 811 58.1804 Table 8 reveals that the highest value is for welding current (A) at level 3, followed by welding speed (D) at level 1, wire feed rate (B) at level 1, and gas flow rate (C) at level 1. The A3B1C1D1 welding settings were selected as the initial parametric configuration for MIG welding stainless steel 316 to AISI 4140 low alloy steel, which has a 3 mm thickness. Table 8 Response table for means of Flexural Strengths Level Welding Current (A) Wire Feed Rate (B) Gas Flow Rate (C) Welding Speed (D) 1 599.7 772.3 757 788.3 2 730.3 692.7 688.3 704.7 3 799.3 664.3 684 636.3 Delta 199.7 108 73 152 Rank 1 3 4 2 The most important element is welding current weight% (Delta = 2.72; Rank 1st), followed by welding speed (Delta = 2.06; Rank = 2nd) and wire feed rate (Delta = 1.54; Rank = 3rd). The (S/N) ratio is least affected by gas flow rate (Delta = 1.16; Rank = 4th) according to the experimental result. The initial ideal parameter combination for increasing the flexural strength of MIG-welded 316 stainless steel with AISI 4140 low alloy steel sheets was A3B1C1D1 based on the larger-the-better flexural strength characteristic. 5.4 Effect of process parameters on flexural strength The influential welding factors on flexural strength are shown in Fig. 18 , It depicts the mean ranking values in each sets The primary effect plot graph and analysis for the S/N ratios indicate the ideal values for optimizing flexural strength, are current at level three (140A), wire feed rate at level one (7cm/s), gas flow rate at level one (7 CFH), and welding speed at level one (20cm/min), with numerical parametric setting of A3B1C1D1. The material's flexural strength decreases as the wire feed rate rises from 1st level to 3rd level. The other element was the gas flow rate; as the gas flow rate increased from 1(one) to 3(three) the flexural strength reduced dramatically. The second most critical welding attribute was the welding speed; as welding speed rises from 1st level to 3rd level the bending strength decreases observed the same investigation as welding speed rises from level one to level two flexural strength increase. Table 18 shows how ANOVA was used to evaluate the appropriateness of the developed models. This technique indicates that the model is insufficient within the confidence limit because, at the intended 95% level of confidence, the calculated F-ratio value of the came up with was lower than the typical F-ratio (F-table value 5.32). The experimental results show that since F-critical = 5.32 is smaller than the F-ratio, all parameters significantly impacted flexural strength. The welding current (47.71%), welding speed (27.65%), and wire feed rate (13.96%) have a highly significant impact, The gas flow rate (6.38%) has a major effect on the bending strength of MIG-welded 316-Stainless Steel with AISI 4140 low alloy steel plates. The errors in statistical models are believed to be regularly distributed in the design of experiments. Furthermore, the p-value (0.065) is larger than 0.05, indicating that the residuals are appropriately distributed and that the variables in the regression model are significant. 6. Conclusion and Recommendation This study was investigated and optimized the factors of automated MIG welding concerning to non-similar metals (SS316 and AISI4140) analyzed using a Taguchi L9 orthogonal array and the signal-to-noise ratio method, and ANOVA was used to determine the influence of each parameter. Tensile strength of 636MPa was recorded by parametrically combining 140A welding current, 7cm/s wire feed rate, 11CFH gas flow rate, and 30cm/s welding speed. According to ANOVA, welding speed and current were the two factors that had the greatest impact on tensile strength. 140A welding current, 10.5cm/s wire feed rate, 7CFH gas flow rate, and 40cm/s welding speed were used in a parametric configuration to produce a maximum hardness of 625HV. ANOVA revealed that welding current was the second-most significant factor for hardness after welding speed. An experimental setup of 140A welding current, 7cm/s wire feed rate, 11CFH gas flow rate, and 30cm/min welding speed resulted in a maximum flexural strength of 692MPa. Welding current, followed by welding speed, was found to be the most crucial component in flexural strength by analysis of variance (ANOVA). To achieve a significant weld width of 6.21 mm, a combination of 125A welding current, 7cm/s wire feed rate, 9CFH gas flow rate, and 40cm/min welding speed was employed. An analysis of variance (ANOVA) it was observed that welding current, followed by welding speed, was the factor that had the greatest impact on weld width. The following recommendation and scope for future work are made based on the study’s findings and conclusion. Particularly in relation to the joint interface area, and application of heat treatment to improve the properties of welded joints produced by automated MIG welding, particularly those involving dissimilar metals such as SS 316 and AISI 4140.Automated MIG welding of SS 316 with AISI 4140 was employed in this study, and it can be extended to other dissimilar materials with varying thickness, Futhermore HAZ and mechanical characterisation with reinforcement can be explored by applying Automated MIG approach. Declarations Ethics declarations Ethical approval Not applicable. Consent to participate Not applicable. Consent to publish All authors have read and approved this manuscript. Author Contributions: Conceptualization T.A.D, H.B; Methodology, T.A.D, H.B and A.A.R.; software, S.Z.K , T.A.D, H.B and A.A.R and G.M.S.A; validation, T.A.D, H.B and G.M.S.A; formal analysis, S.Z.K, A.A.R, A.E, A.A.D and G.M.S.A.; investigation T.A.D, H.B.; resources, S.Z.K, H.B, A.A.R,A.E and G.M.S.A.; writing—original draft preparation, T.A.D and H.B; writing—review and editing, T.A.D, H.B, A.A.D, A.E and G.M.S.A.; visualization, S.Z.K , A.A.R, A.A.D, A.E and G.M.S.A.; supervision, H.B; project administration, S.Z.K, A.A.R, A.E and G.M.S.A; funding acquisition, A.E All authors have read and agreed to the published version of the manuscript. Data availability statement The data that support the findings of this study are available within the manuscript. Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. 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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-3854720","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":267284818,"identity":"eebc44c7-4bd5-4ff7-8db8-8a7e2a7a40c9","order_by":0,"name":"Tajudin Ayub Deko","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA9klEQVRIie2PvQrCMBRGEwJ2iboqDvUR4ihY+iopBadWfARBqIs/q0LBV3ByTsng4gME4uDkJFI3kQ620cEpWQVzhvvd4TvcBACL5TehAJMqMWB5GTXH0MdfCszWlYLMymcAjLhaTIrvsEvrOj65RMSMD557t4EAzO+R5gqmw3ZKLr2dGFEeL2UvQQC1N3vdw+iwgwmHOxERHs8lLJUaquuU5lkpvlL6c+mbldb7SqAU8JCBWRHnsJ8SHm6ON5otJjJMEJxq/+KsokBcC+4tDzHPH4X0trNplt81SnmHquiycsCkWuFE16/uMBWu6hWGssVisfwlL/y6VqohDgWEAAAAAElFTkSuQmCC","orcid":"https://orcid.org/0009-0007-5637-7711","institution":"ASTU: Adama Science and Technology University","correspondingAuthor":true,"prefix":"","firstName":"Tajudin","middleName":"Ayub","lastName":"Deko","suffix":""},{"id":267284819,"identity":"bd992fdf-1037-48b4-919b-b1051ddaee2a","order_by":1,"name":"Habtamu Beri","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Habtamu","middleName":"","lastName":"Beri","suffix":""},{"id":267284820,"identity":"3742e83b-637e-417e-99b0-7c27564fa8fb","order_by":2,"name":"Ali A. 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chemical composition after testing\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-3854720/v1/d1f8691331a2173d12a84b12.png"},{"id":49768084,"identity":"e5370e77-b19c-47e5-91e8-ccba01862c91","added_by":"auto","created_at":"2024-01-17 17:19:20","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":175217,"visible":true,"origin":"","legend":"\u003cp\u003eAdjustment of welding gun angle Metal thickness: 3mm\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-3854720/v1/3482b75bdcfe1869fcd15a6d.png"},{"id":49768087,"identity":"cc639240-6f82-4de9-b772-18474c803113","added_by":"auto","created_at":"2024-01-17 17:19:20","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":199398,"visible":true,"origin":"","legend":"\u003cp\u003eAutomatic MIG welding setup (MIDI, Addis Ababa, Ethiopia)\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-3854720/v1/70bbbda3d590d61c7f6259fd.png"},{"id":49768086,"identity":"6f993b9e-697b-443d-ae3c-7bbf1a3e7661","added_by":"auto","created_at":"2024-01-17 17:19:20","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":301974,"visible":true,"origin":"","legend":"\u003cp\u003eSamples after welded\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-3854720/v1/08386d1995c99d618c5d0059.png"},{"id":49768085,"identity":"09a332b6-76f7-4904-878e-a6e18b543cb5","added_by":"auto","created_at":"2024-01-17 17:19:20","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":121582,"visible":true,"origin":"","legend":"\u003cp\u003eWeld bead\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-3854720/v1/ba06e1f25923952d5e7bde9d.png"},{"id":49770324,"identity":"21dcce3b-a281-4794-bd09-c50f627df485","added_by":"auto","created_at":"2024-01-17 17:35:20","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":183185,"visible":true,"origin":"","legend":"\u003cp\u003eSurface defect measurement after LPT\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-3854720/v1/a4c9b2d928f9e6e9acce8abd.png"},{"id":49769340,"identity":"43e34a89-ccb1-4f39-98bc-c9a99b4dbda2","added_by":"auto","created_at":"2024-01-17 17:27:20","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":668732,"visible":true,"origin":"","legend":"\u003cp\u003eLFT processes: (a) pre-cleaning selected samples no. 3, 7, and 8 by using water and dry it; (b) sprayed samples by penetrant for segregation; (c) application of applying UV light to inspect weld surface.\u003c/p\u003e","description":"","filename":"7.png","url":"https://assets-eu.researchsquare.com/files/rs-3854720/v1/78bff96223e0cf1c55bde7aa.png"},{"id":49770936,"identity":"423bac3b-49d5-4048-bb85-eb3bc1cad06f","added_by":"auto","created_at":"2024-01-17 17:51:20","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":171267,"visible":true,"origin":"","legend":"\u003cp\u003eSpecimen ready for testing its tensile strength, (a) Tensile sample (b) Configuration, and (c) broken specimen after force applied (d) fractured specimens (at Adama Science and Technology University)\u003c/p\u003e","description":"","filename":"8.png","url":"https://assets-eu.researchsquare.com/files/rs-3854720/v1/6c95d26f3a86e5cf14ac5e07.png"},{"id":49768094,"identity":"f5399a19-d389-4e20-88ff-e95732474008","added_by":"auto","created_at":"2024-01-17 17:19:20","extension":"png","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":166649,"visible":true,"origin":"","legend":"\u003cp\u003eSpecimens prepared for hardness test\u003c/p\u003e","description":"","filename":"9.png","url":"https://assets-eu.researchsquare.com/files/rs-3854720/v1/aa3b36adb17b58df70d24a44.png"},{"id":49768090,"identity":"4a7cdddc-1d2c-414a-990e-30c236878dd8","added_by":"auto","created_at":"2024-01-17 17:19:20","extension":"png","order_by":10,"title":"Figure 10","display":"","copyAsset":false,"role":"figure","size":158707,"visible":true,"origin":"","legend":"\u003cp\u003eflexural testing: (a) samples before testing (b) sample under 4-point flexural testing, and (c) sample during testing, (d) samples after testing.\u003c/p\u003e","description":"","filename":"10.png","url":"https://assets-eu.researchsquare.com/files/rs-3854720/v1/04fddf92fa2ca6c8973b1480.png"},{"id":49768100,"identity":"a0bd99be-58e4-417d-88f7-f421c4c9f445","added_by":"auto","created_at":"2024-01-17 17:19:21","extension":"png","order_by":11,"title":"Figure 11","display":"","copyAsset":false,"role":"figure","size":64579,"visible":true,"origin":"","legend":"\u003cp\u003eDefects and their length in sample 3\u003c/p\u003e","description":"","filename":"11.png","url":"https://assets-eu.researchsquare.com/files/rs-3854720/v1/add1648c8846b6e9917bc3f9.png"},{"id":49770688,"identity":"11b96a9a-c3ad-4d0e-ae08-99f97a1bb62f","added_by":"auto","created_at":"2024-01-17 17:43:20","extension":"png","order_by":12,"title":"Figure 12","display":"","copyAsset":false,"role":"figure","size":55677,"visible":true,"origin":"","legend":"\u003cp\u003eVickers hardness of sample no. 7 and 3\u003c/p\u003e","description":"","filename":"12.png","url":"https://assets-eu.researchsquare.com/files/rs-3854720/v1/27c5971aca7f5ad62bf90d6f.png"},{"id":49770686,"identity":"f1e426b9-c26d-470c-b4e1-bd522e74ee90","added_by":"auto","created_at":"2024-01-17 17:43:20","extension":"png","order_by":13,"title":"Figure 13","display":"","copyAsset":false,"role":"figure","size":45287,"visible":true,"origin":"","legend":"\u003cp\u003eStress strain graph of sample 7\u003c/p\u003e","description":"","filename":"13.png","url":"https://assets-eu.researchsquare.com/files/rs-3854720/v1/24108cb8b683604624d5e0be.png"},{"id":49769344,"identity":"07554f6c-b8c3-4ebe-8ed7-932cf2b7ad62","added_by":"auto","created_at":"2024-01-17 17:27:21","extension":"png","order_by":14,"title":"Figure 14","display":"","copyAsset":false,"role":"figure","size":52206,"visible":true,"origin":"","legend":"\u003cp\u003eStress strain graph of sample 3\u003c/p\u003e","description":"","filename":"14.png","url":"https://assets-eu.researchsquare.com/files/rs-3854720/v1/a13a796795e54f7e74e66420.png"},{"id":49769342,"identity":"6408c55a-6b5b-42bf-9db9-04638dbd6003","added_by":"auto","created_at":"2024-01-17 17:27:20","extension":"png","order_by":15,"title":"Figure 15","display":"","copyAsset":false,"role":"figure","size":67241,"visible":true,"origin":"","legend":"\u003cp\u003ePercentage contribution of parameters for tensile strength\u003c/p\u003e","description":"","filename":"15.png","url":"https://assets-eu.researchsquare.com/files/rs-3854720/v1/f8c7e3a26f596be4611068f3.png"},{"id":49768101,"identity":"15879bfc-3740-4dc8-bd6e-0f4e806b98ea","added_by":"auto","created_at":"2024-01-17 17:19:21","extension":"png","order_by":16,"title":"Figure 16","display":"","copyAsset":false,"role":"figure","size":426919,"visible":true,"origin":"","legend":"\u003cp\u003eHardness value of sample 7 trial 1\u003c/p\u003e","description":"","filename":"16.png","url":"https://assets-eu.researchsquare.com/files/rs-3854720/v1/936b9135fd3e9facc0e49439.png"},{"id":49769337,"identity":"6bfeeb7e-4b74-4be9-a7c2-9a6603dd26d3","added_by":"auto","created_at":"2024-01-17 17:27:20","extension":"png","order_by":17,"title":"Figure 17","display":"","copyAsset":false,"role":"figure","size":91301,"visible":true,"origin":"","legend":"\u003cp\u003ePercentage contribution of parameters for hardness\u003c/p\u003e","description":"","filename":"17.png","url":"https://assets-eu.researchsquare.com/files/rs-3854720/v1/1ce4dcbda72315fd081672d7.png"},{"id":49768098,"identity":"b0e248a1-067b-47df-afce-cabea53399d7","added_by":"auto","created_at":"2024-01-17 17:19:20","extension":"png","order_by":18,"title":"Figure 18","display":"","copyAsset":false,"role":"figure","size":72038,"visible":true,"origin":"","legend":"\u003cp\u003ePercentage contribution of parameters for flexural strength\u003c/p\u003e","description":"","filename":"18.png","url":"https://assets-eu.researchsquare.com/files/rs-3854720/v1/77cb94533153cf26205a172f.png"},{"id":55236004,"identity":"9c32f34a-c9b4-4f25-aade-31f63dcce70f","added_by":"auto","created_at":"2024-04-24 13:51:18","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":6025332,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3854720/v1/630caff0-eeef-4f94-8f10-c70c45708cff.pdf"}],"financialInterests":"","formattedTitle":"\u003cp\u003eExperimental Investigation and Parametric Optimization of Automated MIG welding Stainless Steel SS316 with Low Alloy Steel AISI 4140 on Mechanical Properties\u003c/p\u003e","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eWelding is a method of joining two similar or dissimilar metals or nonmetals using pressure or non-pressure. MIG welding produces little material waste and can be semi or fully automated. The MIG welding technique is ideal for joining materials during structure fabrication. The process can be used semi-automatically, automatically, or robotically; because of its ability to produce repeatable joints, the process has the potential to be used in mass production units. Automated MIG welding follows the same basic principle as manual or semi-automated MIG welding, which involves using a consumable wire electrode and a shielding gas to melt and join metals together at the weld joint[1\u0026ndash;4]. The electrode wire's end is melted by the arc and then moved to the molten weld pool. It is crucial to note, however, that the actual temperature achieved by the arc will be determined by various parameters, including the welding current, voltage, and wire feed speed, as well as the kind and thickness of materials being welded. The research motivation behind this study is to explore the optimal parameters for automated MIG welding of dissimilar metals, particularly stainless-steel SS 316 and low alloy steel AISI 4140. [5\u0026ndash;9] Achieving a reliable joint between two different metals with varying thermal properties can be challenging, and automated MIG welding is one solution to this problem. Poor weld quality and mechanical qualities, such as weak interfaces, porosity, and cracking, can arise from insufficient selection and optimization of automated Metal Inert Gas (MIG) welding process parameters.[10\u0026ndash;16] The objective of the present research work is to weld SS316 stainless steel with AISI 4140 low alloy medium carbon steel by automated MIG and Welding stainless steel SS 316 with low alloy steel AISI 4140 presents difficulties owing to variations in chemical and physical properties, which can result in poor weld quality and mechanical qualities. This work intends to provide a dependable solution for welding these dissimilar metals and improve the weld quality and mechanical characteristics of the joints by performing experimental investigation and statistical analysis to optimize the MIG welding settings.\u003c/p\u003e"},{"header":"2. Literature Review","content":"\u003cp\u003eThere have been numerous studies regarding dissimilar metals welding by various method of welding. Some of these studies are presented in the following section. conducted a study on dissimilar weldments and found that the GTA welding method can weld with and without filler in AISI-4140 and AISI-316 combination of metals[17\u0026ndash;19]. In this welding current has been found to increase as the wire feed is increased, with increasing voltage, the value of welding current decreases slightly. This factor is crucial in MIG welding because it affects how quickly droplets of metal cross the arc, which in turn defines the type of metal transfer. Each test sample is visually inspected for visual weld defects, weld bead reinforcement over base metal and weld metal penetration into the root of the weld groove. In one of the study focusing on optimizing welding process variables using MIG welding, the goal was to investigate the hardness of non-similar metal welding joints of Stainless steel 304 and C-25 steel by using 0.8 mm diameter stainless steel filler wire. [20\u0026ndash;24] in this study GMAW was used to weld stainless steel AISI 304 and medium carbon steel 45C8. Yield strength, ultimate tensile strength, weld zone hardness, weld bead thickness, and welded joint reinforcement have all been reported. Welding current, welding voltage, gas flow rate, and welding speed are the factors used. The Taguchi Method was used for the experiment design, ANOVA and S/N ratio analysis was performed. To summarize, automated MIG welding is an efficient method for connecting different metals and steels. According to research, choosing the right factors, such as joint design, filler metal selection, welding settings, shielding gas composition, and heat treatment, may help assure high-quality welds[25\u0026ndash;29]. There is a significant gap in information regarding the best parameters and process conditions for automated MIG welding of stainless steel SS316 with low alloy steel AISI4140. More research is required to determine the effect of variables such as heat input, filler metal selection, preheating method, and cooling rate on the mechanical properties and microstructure of the weld, and this knowledge gap can lead to significant improvements in the efficiency and reliability of the welding process, allowing industries to meet their demanding structural requirements. According to a review of these studies, a number of studies on austenitic stainless steels have been conducted [30\u0026ndash;32]. The research concentrated on a specific application, different grades of material, and the optimization of a single welding parameter. While they have the same chemical composition and large applications, no such focus was given to SS316 and low-alloy steel AISI 4140 by considering the effect of wire feed rate and gas flow rate during welding and optimization of weld quality for dissimilar metals.\u003c/p\u003e"},{"header":"3. Materials and Methodology","content":"\u003cp\u003eFollowing a review of the literature, proper base materials and filler material selection, controlling process parameters, research and methodology, electrode wire, and welding conditions are selected. The approach for this study comprises of the use of an automated Metal Inert Gas (MIG) welding procedure to combine stainless steel SS 316 with low alloy steel AISI 4140. Welding parameters, including current, welding speed, wire feed speed, and gas flow rate are changed within a set range [33\u0026ndash;35]. Experimentation and statistical analysis are then used to evaluate the weld quality and mechanical qualities. Welding fixtures are utilized during the experiment to keep the materials in place and guarantee exact weld joint preparation. The resultant welds are then subjected to tensile strength, impact flexural bending test, and hardness testing to determine their appropriateness for practical applications[36]. The results of these tests are examined statistically to determine the best set of welding settings for producing high-quality welds with minimal distortion. The workpiece materials employed in this experiment were stainless steel (SS316) and low alloy steel (AISI4140). Type SS316 is austenitic chromium-nickel stainless steel with a molybdenum content of 2\u0026ndash;3%. The presence of molybdenum improves corrosion resistance as well as strength at maximum temperatures. Furnace components, heat exchangers, jet engine parts, pharmaceutical equipment, valves, chemical equipment, tanks, evaporators, pulp, paper, and textile processing equipment are typical applications. Low alloy AISI 4140 steel is made up of chromium, molybdenum, and manganese. Due to its durability, high fatigue strength, abrasion and impact resistance, it has been widespread across many industrial sectors. Chromium and molybdenum are added to increase corrosion resistance. Molybdenum can be especially helpful when attempting to stave against corrosion brought on by chlorides. The chemical composition test of the base and weld metals was performed at the Ethiopian Quality and Standard Agency using SPECTRO Maxx LMX10 ARC/SPARK spectrometer features a single air optic, with CMOS sensors. It extends the relevant and applicable elemental wavelength range from 233 to 670 nm. Figure\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e below shows the tested samples under spectroscopy. Recognizing that carbon identification is of primary concern in all material functions during welding. Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. Shown below is Chemical composition of studied base metals.\u003c/p\u003e \u003cp\u003e \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 of studied base metals\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"11\"\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 \u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMaterials\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"10\" nameend=\"c11\" namest=\"c2\"\u003e \u003cp\u003eChemical composition (wt. %)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eElements\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMn\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eSi\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eCr\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eNi\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eMo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eFe\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBase SS316\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e12.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e2.40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e65.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMetals AISI4140\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e96.8\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. shown below is the Mechanical properties of SS316 and AISI4140, ER-309L stainless steel filler material with a diameter 1.2mm was employed as filler material. ER-309L basically it is an Austenitic stainless steel 309L is used to join stainless steel sheets from the 300 series, including 309, 309L, 304, 316, and others. The weld deposit is more resistant to inter-granular corrosion caused by carbide precipitation, which may happen if using straight 309 grade because of the lower carbon content.\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\u003eMechanical properties of SS316 and AISI4140\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eMechanical properties\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eValue\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eValue\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSS 316\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAISI 4140\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUltimate tensile strength\u003c/p\u003e \u003cp\u003eYield strength\u003c/p\u003e \u003cp\u003ePercentage elongation (in 50mm)\u003c/p\u003e \u003cp\u003eRockwell hardness B\u003c/p\u003e \u003cp\u003eMean Coefficient of thermal expansion(0-649\u003csup\u003eo\u003c/sup\u003eC)\u003c/p\u003e \u003cp\u003eThermal conductivity (@100\u003csup\u003eo\u003c/sup\u003eC)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e579MPa\u003c/p\u003e \u003cp\u003e290MPa\u003c/p\u003e \u003cp\u003e50%\u003c/p\u003e \u003cp\u003e79HRB\u003c/p\u003e \u003cp\u003e(18.5) \u0026micro;m/m*K\u003c/p\u003e \u003cp\u003e22.3W/mK\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e655MPa\u003c/p\u003e \u003cp\u003e415MPa\u003c/p\u003e \u003cp\u003e25.70%\u003c/p\u003e \u003cp\u003e92HRB\u003c/p\u003e \u003cp\u003e12.2\u0026micro;m/m\u003csup\u003eo\u003c/sup\u003eC\u003c/p\u003e \u003cp\u003e42.6W/mK\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\u003eShielding gas used pure argon it is most beneficial for maintaining the stability of an arc. Argon also helps with a narrower penetration, So it produces a cleaner weld. During the welding process, metals are exposed to temperatures around 700\u003csup\u003e0\u003c/sup\u003eC. Argon is used to protect the molten pool of metal against elements in an atmosphere. Depending on the material being welded and the choice of shielding gas, several welding voltage ranges are available for MIG welding. As a general rule, the MIG welding voltage range falls between 16 and 22 volts. Due to the fact that welding voltage was not chosen as a process parameter for optimization in this experiment, a best value welding voltage of 20V was chosen by taking into account the aforementioned criteria. The majority of industry professionals advise choosing an arc length for MIG welding that is between 9.5mm and 13mm, or about the diameter of the electrode being used, type and thickness of the material being welded, welding location, wire diameter and the feed rate are all critical aspects. Because MIG welding is automated in this experiment, it is easy to set the arc length, and 11mm is chosen as the arc length. Depending on the thickness of the material being welded, welding location, type of joint being utilized, the appropriate root gap size for a MIG welding joint may change. After taking those factors into account and doing a trial exercise, the optimal welding penetration was found to be 1.2mm. This root gap is chosen for this experiment. Depending on the thickness of the base metal and the welding position, the ideal welding gun or work angle must be chosen. The welding operator should hold the MIG welding gun at a 90-degree work angle when welding a butt joint, which is a 180-degree junction. The integrated automated MIG welding machines used in this experiment has a welding gun holder with a work angle arrangement that is simple to adjust to the necessary angle value. For this experiment, the welding gun angle is set at 90\u003csup\u003e0\u003c/sup\u003e to the base metals as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e below.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Experimental Set-up\u003c/h2\u003e \u003cp\u003eExperiments are being carried out at the Manufacturing Technology and Engineering Industry Research and Development Center at the welding training center workshop on the FIMER standard TM 350 MIG welding machine integrated with a welding gun fixture machine that facilitates the automatic MIG welding process, as shown in the Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e. Automatic MIG welding is a process that can be used to control welding speed and achieve more consistent welds. The weld joint is positioned by a human operator, but the MIG gun is often mounted on a welding gun fixture or welding carriage that is controlled by a control panel or adjustment board. The control panel knobs can be used to set the exact speed at which the electrode wire should be fed, ensuring that every weld is completed at the same rate. Automatic MIG welding can also include additional features such as adaptive control, which monitors and adjusts the weld in real-time as conditions change. In this experiment, an automatic welding mechanism is used because semi-automatic welding with MIG welding equipment has limitations. The gun moves at an assigned rate during automatic operation, which helps to regulate and establish the welding speed. When the arc length changes, the operation's voltage and current both must change at the same time. Controlling the arc length and welding speed is necessary for better continuous welding quality as well as for the operator to select the welding parameters, such as welding current and voltage, in accordance with the Taguchi design orthogonal array. In order to take welding speed into consideration as one of the welding parameters, adopting automatic MIG welding was ultimately more beneficial than manual MIG welding.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eWelding fixtures are utilized in this experiment to help manage the root gap of welding and eventually improve the weld quality. This is crucial since the root gap may have an impact on the weld's penetration and strength. Welding fixtures are made to accommodate particular sizes and shapes of workpieces. This guarantees that each workpiece will keep a constant distance and the final weld will be of higher quality and have more penetration.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Preparation of Samples and Experimental Condition\u003c/h2\u003e \u003cp\u003eWelded samples are shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e, stainless and low alloy steel sheets with dimensions of 100mm\u0026times;75mm\u0026times;3mm were made with a shear cutting machine called E21S ESTUN. Before welding, the edges of the work components were suitably prepped. According to Taguchi's Design of Experiments, the L9 orthogonal array was used to determine process parameters such as welding current, wire feed rate, gas flow rate, and welding speed at three different levels. Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, for the experiment is displayed beneath the L9 orthogonal array. The welding process parameters and their levels are chosen according to the general approach which entails conducting a review of the literature and trial exercises to establish the best parameters based on elements such material thickness, joint geometry, and desired weld quality.\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\u003e Selected process factors and their levels\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\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=\"char\" char=\".\" 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=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eS. No\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eProcess factors\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eUnits\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c6\" namest=\"c4\"\u003e \u003cp\u003eLevels\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWelding current\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAmp (A)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e110\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e125\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e140\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWire feed rate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ecm/s\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e10.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGas flow rate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCFH\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWelding speed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ecm/min\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e40\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\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Weld Bead Width Geometry\u003c/h2\u003e \u003cp\u003eThe geometry of the weld bead is the primary determinant of MIG weld quality. Parameters of the welding process, such as welding current, shielding gas flow rate, welding speed, wire feed rate, and gap distance have a significant impact on bead geometry. Important factors in determining the mechanical features of the weld include the penetration and area of penetration, the heat-affected zone, the bead width, height, and penetration, in addition to the mechanical properties like strength of tensile, hardness, and bending strength. Each welded sample's width was measured using the 0.01 mm count method to determine the bead width. Comparable calipers. The ideal weld bead size for a given application can vary depending on a number of variables, including the base metal's thickness, composition, joint design and the intended use of the welded product. Due to the fact that each welded sample's width varied depending on the factors employed, the weld with the smallest bead width was taken as to be a better weld because it produced a reduced heat-affected zone when there was sufficient welding penetration. Bead width of the weld is illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e3.4 Welding defects Acceptance Criteria\u003c/h2\u003e \u003cp\u003eThe acceptability criteria for defects in welding such as porosity, cracks, absence of fusion, slag, and excessive reinforcement are described in AWS D1.1/D1.1M:2020 and ASME Section VIII []. These criteria are based on elements such as the size, position, and direction of the faults. The maximum allowed lengths for each type of fault are as follows: According to AWS D1.1/D1.1M:2020, cracks should not exceed 3mm (1/8 in), porosity should not exceed 5% of total area in a 100 mm (4 in) length of weld or 1% in a 25 mm (1 in) length of weld, lack of fusion should not exceed 3mm (1/8 in) in a single pass weld, lack of fusion should not exceed 3mm (1/8 in) in a single pass weld, slag inclusions should not exceed a depth of 3mm (1/8 in) or width of 6mm (1/4 in), and Excessive reinforcement should not be more than three times the thickness of the base metal or 19mm (3/4 in), whichever is less. ASME Section VIII also specifies defects limitations, which may differ somewhat from those defined in AWS D1.1/D1.1M:2020 depending on the application and needs (American National Standard Structural Welding, 2020). The acceptance criteria for surface defects in welding that allows a maximum percentage of defects of less than 5% is typically specified in welding standards and codes such as ISO 5817:2014, AWS D1.1/D1.1M:2020, and ASME Section VIII. The percentage of surface are calculated by the ratio of the sum of the area of surface defects to the total surface area of welded zone (weld bead). POSD = \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\frac{\\sum Ad}{Aw}\\)\u003c/span\u003e\u003c/span\u003ex100%, Where Ad\u0026thinsp;=\u0026thinsp;area of each surface defects and Aw\u0026thinsp;=\u0026thinsp;surface area of weld zone\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e3.5 Welding Defects Using Non-Destructive Testing (NDT)\u003c/h2\u003e \u003cp\u003eManufacturing, construction, and other industries that rely on welded connections frequently experience welding defects. These defects may jeopardize the joint's structural integrity and cause failures, which might be harmful in applications like pipelines, pressure vessels, ships, and bridges. Non-destructive testing (NDT) techniques offer a way to find defects in welding without causing damage to the welded connection, enabling prompt diagnosis and remedial action. In this study this study investigated the application of NDT techniques, particularly liquid penetrant testing (LPT) and liquid fluorescent testing (LFT), to detect and classify welding flaws. The many types of welding flaws, their causes, and the factors influencing their occurrence were also covered. After the test is operated and the type of defects are identified by the visual inspection method, and then the length of these defects is measured as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e below for further analysis.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e3.6 Liquid Penetrant Test (LPT)\u003c/h2\u003e \u003cp\u003eTesting with Liquid Penetrants (LPT) is a non-destructive testing method used to locate surface-breaking discontinuities in a wide range of materials, including metals, polymers, and ceramics. It entails putting a liquid penetrant to the surface of the material, letting it to seep into small gaps and fractures, and then removing the surplus penetrant before applying a developer that pulls the penetrant out of the surface. LPT has several applications in areas such as aerospace, automotive, and manufacturing. It is especially effective for identifying surface cracks, porosity, laps, seams, and other defects that might compromise the integrity of a material or component. During manufacturing or maintenance, LPT is commonly used to examine welds, castings, forgings, and machined components. The process of conducting an LPT typically involves several steps. The penetrant is applied over the entire surface using a spray bottle, brush, or dipping. The next step is applying a dry cloth to remove any excess penetrant from the surface. This process also aids in the removal of any non-penetrated liquid on the surface, which might cause misleading signals during application. Then, the penetrant begins to migrate out, revealing defects wherever there are traces of penetrant. Finally, faults discovered are reported and the part can be evaluated further.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e3.7 Liquid Fluorescent Test (LFT)\u003c/h2\u003e \u003cp\u003eThe Liquid Fluorescent Test (LFT) is a sophisticated Non-Destructive Testing (NDT) method that is widely used to detect flaws in metals. It works on the same principles as LPT, with a liquid penetrant applied to the material's surface. LFT, on the other hand, employs a fluorescent penetrant that fluoresces under UV light, allowing it to identify extremely tiny defects and crack discontinuities. The procedure for doing an LFT begins with surface preparation, which includes eliminating any residues of oil or grease that may function as barriers to the penetrant. The surface is then sprayed or coated with a luminous liquid penetrant. A black light illuminator is used to highlight any fluorescing indications on the surface under proper lighting circumstances. This aids in identifying possible cracks and indicators, which may be documented and investigated further. All defects observed are shown in Figs.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e3.8. Tensile Strength\u003c/h2\u003e \u003cp\u003eMIG-welded experimental specimens made from SS316 and AISI4140 were prepared for various mechanical testing and machined in accordance with ASTM to establish the requisite dimensions for examining their properties. For conducting Tensile test Universal testing machine, a max force of 50 KN; at Adama Science and Technology University. Figure\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003ea\u0026ndash;d demonstrates how the specimen was made, and how the specimens were broken after the tensile test.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e3.9. Hardness Testing\u003c/h2\u003e \u003cp\u003eUsing calibrated hardness-testing equipment, the hardness of a welded specimen is determined by pressing a hardened steel ball or diamond point into its flat surface while applying a predefined load. The size of the indentation that occurs is then measured. Hardness testing was carried out at the material engineering laboratory of Adama Science and Technology University utilizing a Vickers hardness testing equipment. The resulting impression was measured and analyzed under a microscope before being converted into a hardness number. The samples were sectioned for hardness testing using an abrasive cutter as shown in the Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e3.10 Flexural Testing\u003c/h2\u003e \u003cp\u003eThe maximum stress tolerance of a material is measured by its flexural strength, which is a force (measured in newton) per unit area. Flexural test performed at Adama Science and Technology University. Follow these procedures to run this test: first, to get accurate findings, cut a rectangular bar-shaped sample out of the material, making sure that the length is at least four times the breadth. The samples dimensions with 3mm x 18mm x 130mm are prepared as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e10\u003c/span\u003e(a-d) given below. Second, position the sample on two lower supports with the standard-recommended distance (L) between them. Utilize two top loading points that are more closely spaced apart than the bottom supports to apply the weight. Third, until the sample cracks or gets the specified deflection, apply a load at a steady pace. Keep track of the load and deflection readings during the test. Fourth, use the following formula to get the material's flexural strength: Flexural strength is calculated as The 4-point test formula for determining a rectangular specimen's flexural strength is:\u003c/p\u003e \u003cp\u003eσ\u0026thinsp;=\u0026thinsp;3LF/(2bd\u003csup\u003e2\u003c/sup\u003e) (1)\u003c/p\u003e \u003cp\u003ein which b\u0026thinsp;=\u0026thinsp;specimen width, d\u0026thinsp;=\u0026thinsp;specimen thickness, L\u0026thinsp;=\u0026thinsp;specimen length, and F\u0026thinsp;=\u0026thinsp;total force applied to the specimen by two loading pins. Fifth, use the following calculation to get the stress at maximum load: Stress is calculated as (M*c)/ (I*y), where M is the maximum bending moment, c is the vertical distance in either compression or tension from the neutral axis to the extreme fiber, I is the moment of inertia of the sample's cross-section, and y is the perpendicular distance from the centroid axis to the extreme fiber. In materials science and engineering, the four-point bending test is frequently employed to evaluate the strength and stiffness of many materials, including plastics, composites, ceramics, and metals.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"4. Taguchi Orthogonal Array","content":"\u003cp\u003eIn this experiment, the welding current, wire feed rate, gas flow rate, and welding speed were all investigated. The total degrees of freedom of the process parameters in the Taguchi DOE concept determines the utilization of an orthogonal array. The degree of freedom is accounted for or defined as the quantity of comparisons necessary to maximize each parameter (factor) with the chosen levels. Numerical analysis and experimental design of process parameters are created using Minitab 21 statistical software with four elements and three levels of the experiment. Three levels of study were conducted to examine the effects of welding parameters on tensile strength, hardness, flexural strength, and weld bead width while MIG welding dissimilar 316 stainless steel with AISI 4140 low alloy steel. The total number of degrees of freedom provided must be greater than the number of experiments required to look into the variables. In this study, the choice of orthogonal array was determined by counting the layers of different factors. Three levels were selected for each of the four factors in the experiments. DOF\u0026thinsp;=\u0026thinsp;P*(L-1), in which DOF\u0026thinsp;=\u0026thinsp;Degree of freedom, P\u0026thinsp;=\u0026thinsp;Number of factors and, L\u0026thinsp;=\u0026thinsp;Number of levels, DOF\u0026thinsp;=\u0026thinsp;4(3\u0026thinsp;\u0026minus;\u0026thinsp;1)\u0026thinsp;=\u0026thinsp;8 (OA\u0026thinsp;\u0026gt;\u0026thinsp;DOF, OA has 9 experimental runs). The L9 orthogonal array was chosen for the experiment design because the total number of orthogonal array (OA) experiments should be more than or equal to the computed value.\u003c/p\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003e4.1 Signal to noise ratio (S/N ratio)\u003c/h2\u003e \u003cp\u003eThe Taguchi approach highlights the importance of analyzing response variation using the signal-to-noise ratio because it lessens the impact of uncontrollable parameter fluctuation on quality features. In order to evaluate the quality characteristic that deviates from the ideal value, Taguchi employed the S/N ratio, which is equal to the average to SD ratio. Depending on the type of feature, there are a variety of S/N ratios accessible; smaller is preferable, nominal is preferable, and bigger is preferable. Larger the better (Maximization), Smaller the better (Minimization), Nominal the best. Standard S/N ratio formula is given by \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\frac{S}{N}\\)\u003c/span\u003e\u003c/span\u003e Ratio = -10 \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\left[\\frac{1}{n} \\sum _{j=1}^{n}\\frac{1}{{Y}_{{ij}^{2}}}\\right]\\)\u003c/span\u003e\u003c/span\u003e, Where 'i' is the number of a trial; 'Yi' is the measured value of quality characteristic for the ith trial and jth experiment, 'n' is the number of repetitions for the experimental combination.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003e4.2 Analysis of Variance (ANOVA)\u003c/h2\u003e \u003cp\u003eANOVA is a statistical approach for determining the impact of numerous control conditions. The percentage contributions of each control factor are utilized in this research to quantify their influence on performance attributes. This analysis took a 5% significance level (or a 95% level of confidence) into account. A significance level of less than 0.05 is deemed significant in ANOVA analysis, whereas a value greater than 0.05 is not. In the analysis of variance, the percentage contribution of each influential parameter was computed using the following formula \u003cimg src=\"https://myfiles.space/user_files/122228_c8a1650c59388082/122228_custom_files/img1705486166.png\"\u003e\u0026nbsp;Percentage contribution of individual factor, SS: Sum of squares or treatment sum of squares, SST: Total sum of squares.\u003c/p\u003e\u003c/div\u003e"},{"header":"5. Results and Discussion","content":"\u003cp\u003eThe effective of welding aspects on the mechanical characteristics of the non-similar weld of SS-316 stainless steel with AISI-4140 low alloy steels was determined. the welded samples were visually inspected for welding defects using liquid penetrant.\u003c/p\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003e5.1 Liquid Penetrant and Visual Defect Testing\u003c/h2\u003e \u003cp\u003eAll samples are analyzed in terms of the type of defects and their length by calculating the surface area of each defect on the weld zone divided by total surface area of weld zone to get the percentage of defects on the surface. According to AWS D1.1/D1.1M:2020, and ASME Section VIII, the acceptance criteria for surface defects in welding that allows a maximum percentage of defects of less than 5% is typically specified in welding standards. These standards define the acceptable levels of various types of welding defects, such as porosity, undercutting, and small crack. The acceptance criteria for welding flaws such as cracks, porosity, lack of fusion, slag, and excessive reinforcement are defined in AWS D1.1/D1.1M:2020 and ASME Section VIII based on their size, location, and orientation. The maximum allowable defect lengths for each category are as follows: -\u003c/p\u003e \u003cp\u003e \u003cem\u003e(a)Small cracks\u003c/em\u003e \u003c/p\u003e \u003cp\u003eSamples 2, 3, 4 and 6 exhibited a small crack. This defect appeared as a result of slag inclusion due to the welding speed is too fast. AWS D1.1/D1.1M:2020 specifies that If the crack is due to slag inclusion then the maximum length will be around 1/8 in (3 mm). Therefore, all samples with small crack are not exceed 3mm and accepted.\u003c/p\u003e \u003cp\u003e \u003cem\u003e(b)Porosity\u003c/em\u003e \u003c/p\u003e \u003cp\u003eSamples 1 and 8 exhibited a porosity. This defect was caused by trapped air in the shielding gas, which caused distributed porosity and gross surface pore breaking. According to AWS D1.1/D1.1M:2020 and ASME Section VIII, the maximum diameter of porosity in welds should not exceed 3/32 inches (2.4 mm) for most applications. Therefore sample 1 and 8 have porosity diameter of 0.8mm and 1mm respectively they are accepted.\u003c/p\u003e \u003cp\u003e \u003cem\u003e(c)Lack of fusion\u003c/em\u003e \u003c/p\u003e \u003cp\u003eSamples 3, 4 and 5 exhibited a lack of fusion. This defect appeared as a result of low current input, electrode angle, arc length, electrode manipulation, and incorrect welding parameter settings, according to AWS D1.1/D1.1M:2020 all lack of fusion defects are accepted except lack of fusion in sample 3 which has 6.4mm in length.\u003c/p\u003e \u003cp\u003e \u003cem\u003e(d)Excessive reinforcement\u003c/em\u003e \u003c/p\u003e \u003cp\u003eSamples 2 and 3 exhibited an excessive reinforcement. This defect was caused at lower welding current and higher wire feed rate. Therefore, they are accepted. Sample 3 having the highest POSD due to the lower welding current, high wire feed rate, and high feed rate. The percentage of surface defect has been calculated for sample 3 using Fig.\u0026nbsp;\u003cspan refid=\"Fig11\" class=\"InternalRef\"\u003e11\u003c/span\u003e of the report of all samples obtained from the liquid penetrant testing laboratory center at the Manufacturing Technology and Engineering Industry Research and Development Center, Ethiopia.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cem\u003e(e)Micro-hardness Analysis\u003c/em\u003e \u003c/p\u003e \u003cp\u003eOn both sides of sample 3 and sample 7, the differences in Vickers micro-hardness values are shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig12\" class=\"InternalRef\"\u003e12\u003c/span\u003e according to the separation between the base metals and the weld center. The hardness values of all the weld regions were considerably higher than the base metals' hardness. The area that is welded is the hardest.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003e5.2 Mechanical Properties\u003c/h2\u003e \u003cp\u003eInvestigating and improving automated MIG welding process parameters for welding SS 316 and AISI 4140 with dissimilar metals was the aim of this study. It is accomplished by inspecting the mechanical attributes (tensile, hardness, flexural bending attributes. Following that, the Taguchi method was used to examine the test response parameters, and analysis of variance (ANOVA) was used to calculate the percentage contribution of each parameter.\u003c/p\u003e \u003cdiv id=\"Sec20\" class=\"Section3\"\u003e \u003ch2\u003e5.2.1 Tensile Strength Result Analysis\u003c/h2\u003e \u003cp\u003eA universal testing equipment was used to acquire tensile strength data. Each test included two specimens, with the average value being taken. Table\u0026nbsp;\u003cspan refid=\"Tab7\" class=\"InternalRef\"\u003e7\u003c/span\u003e shows the measured experimental results of the mean tensile strength values for 15each trial experiment. The failure of the testing sample following the test on the stainless steel and in sample 2 and sample 3 failure occur at weld zone which shows the tensile strength of low alloy steel is better than stainless steel. Figures\u0026nbsp;\u003cspan refid=\"Fig13\" class=\"InternalRef\"\u003e13\u003c/span\u003e and \u003cspan refid=\"Fig14\" class=\"InternalRef\"\u003e14\u003c/span\u003e below shows the stress strain graph for maximum and minimum tensile strength appeared in sample 7 and sample 3 respectively.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eAs indicated in Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e, the most affected parameters were level three welding current (A), which was followed by welding speed (D) at level one, wire feed rate (B) at level one, and gas flow rate (C) at level one. Taguchi utilized these welding factors (A3B1C1D1) as the first parametric configuration for the stainless steel 316 MIG welding to low alloy steel 4140 with a thickness of 3 mm. This implies that it is possible to employ that as a parameter combination in order to generate larger and better outcomes.\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\u003eResponse table for Means of Tensile Strength\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\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=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLevel\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWelding Current(A)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eWire feed rate (B)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eGas flow rate (C)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eWelding speed (D)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e568\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e617\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e608\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e618\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e603.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e598.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e603\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e603.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e626.7\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e582.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e587\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e576.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDelta\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e58.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e34.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e41.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRank\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\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2\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 numerical value at the maximum point in each graph of the main effect plot shown above Fig.\u0026nbsp;\u003cspan refid=\"Fig17\" class=\"InternalRef\"\u003e17\u003c/span\u003e denotes the factor's optimal value at indicated level. The optimal parameters (A3B1C1D1) were welding current at level three (140A), wire feed rate at level one (7cm/s), gas flow rate at level one (7 CFH), and welding speed at level one (20cm/min). According to the signal-to-noise ratio value in Table\u0026nbsp;9, the welding current weight percentage has the greatest influence, (Delta\u0026thinsp;=\u0026thinsp;0.87; Rank\u0026thinsp;=\u0026thinsp;1st) followed by welding speed (Delta\u0026thinsp;=\u0026thinsp;0.63; Rank\u0026thinsp;=\u0026thinsp;2nd) and wire feed rate (Delta\u0026thinsp;=\u0026thinsp;0.53; Rank\u0026thinsp;=\u0026thinsp;3rd). The (S/N) ratio is least affected by gas flow rate (Delta\u0026thinsp;=\u0026thinsp;0.34; Rank\u0026thinsp;=\u0026thinsp;4th) according to the experimental result. The initial best parameter combination for increasing weld metal strength with the greater-the-better responsiveness of automated MIG-welded of 316 stainless steel sheets with 4140 low alloy steel is A3B1C1D1. Figure\u0026nbsp;\u003cspan refid=\"Fig18\" class=\"InternalRef\"\u003e18\u003c/span\u003e shows how these variables affect tensile strength. According to the graph when the welding current rises from level 1(one) to level 3(three), the strength of tensile increases significantly. The tensile strength value decreases from level one to level three as the wire feed rate rises, as well as the tensile strength value decrease from level one to level three when gas flow rate and welding speed rises observed the same outcome.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec21\" class=\"Section3\"\u003e \u003ch2\u003e5.2.2 Analysis of Variance (ANOVA) for tensile strength\u003c/h2\u003e \u003cp\u003eANOVA was used to assess the appropriateness of the created models. The model is stated to be adequate within the confidence limit if the estimated value of the F-ratio of the produced model is less than the standard F-ratio (F0.05, 1, 8\u0026thinsp;=\u0026thinsp;5.32 from analysis ) value at the required level of confidence of 95%. Table\u0026nbsp;10 shows how analysis of variance (ANOVA) is used to identify the most important characteristics that affect the tensile strength of MIG-welded steels. All parameters have a significant impact on tensile strength because the F-value from the table is 4.7 and the F-ratio from the ANOVA table is significantly higher than the critical F-ratio. As illustrated in the Fig.\u0026nbsp;\u003cspan refid=\"Fig15\" class=\"InternalRef\"\u003e15\u003c/span\u003e, the weight percentage of welding current (49.66%), Welding speed (24.65%), Wire feed rate (17.34%), and Gas flow rate (6.36%) all have a significant effect on the tensile strength of automated MIG-welded 316 stainless steel with 4140 low alloy steel sheets with a thickness of 3 mm, as indicated in the table above. The ANOVA analysis, including p-values, indicates that the percentage of contribution from all parameters significantly impacts the tensile strength as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig15\" class=\"InternalRef\"\u003e15\u003c/span\u003e, (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05). The R-squared value, which measures the model's fit to the data, is 98.01% according to table 10, indicating a strong fit.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe errors in statistical models are believed to be regularly distributed in the design of experiments. A normal probability plot of the residuals is used to evaluate the importance of effects. The residuals' normal probability plots reveal that they are pretty near to the normal probability lines. Furthermore, the p-value (0.052) is larger than 0.05, indicating that the residuals are normally distributed and that the variables in the regression models are both significant and appropriate.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec22\" class=\"Section2\"\u003e \u003ch2\u003e5.3 Hardness Test Results Analysis\u003c/h2\u003e \u003cp\u003eHardness measurements were taken with a Vickers hardness test machine, every test being run on the weld zone, HAZ, and base metal. Each zone had three readings, and the average value was computed. The Fig.\u0026nbsp;\u003cspan refid=\"Fig16\" class=\"InternalRef\"\u003e16\u003c/span\u003e below show when Vickers hardness test machine display first trial of sample 7 which is 541HV. Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e displays the average hardness values for each trial experiment as determined by the measured experimental results. For superior performance qualities, the larger hardness was predicted to be the preferred alternatives.\u003c/p\u003e \u003cp\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\u003eMean hardness testing result\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=\"char\" char=\".\" 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=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSample No\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWelding Current (A)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eWire feed rate (B)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eGas flow rate (C)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eWelding speed (D)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eHardness (HV)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eSignal to noise ratio\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e110\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e482\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e53.6609\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e110\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e435\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e52.7698\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e110\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e456\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e53.1793\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e125\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e599\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e55.5485\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e125\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e435\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e52.7698\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e125\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e482\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e53.6609\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e140\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e541\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e54.6639\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e140\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e625\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e55.9176\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e140\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e476\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e53.5521\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 welding parameters are slected such as welding current (A) at level 3, wire feed rate (B) at 1st level, gas flow rate (C) at 1st level, and welding speed (D) at 3rd level have the greatest value of hardness. According to the experimental response data, these welding settings (A3B1C1D3) were selected as the initial parametric hardness configuration when automated MIG-welding AISI 4140 low alloy steel with a 3 mm thickness to stainless steel 316. Because the response is larger the better, the major influence plot for control parameters for mean hardness value is essentially the same as the main effects plot for SN ratios, therefore the impacts of the factors on hardness are evaluated from the main effect plot for SN ratios.The welding speed weight percentage had the biggest impact (Delta\u0026thinsp;=\u0026thinsp;1.55; Rank\u0026thinsp;=\u0026thinsp;1st), followed by welding current (Delta\u0026thinsp;=\u0026thinsp;1.51; Rank\u0026thinsp;=\u0026thinsp;2nd), and wire feed rate (Delta\u0026thinsp;=\u0026thinsp;1.16; Rank\u0026thinsp;=\u0026thinsp;3rd), as shown by the value of the ratio of signal to noise in Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e. The gas flow rate had the least impact on the S/N ratio (Delta\u0026thinsp;=\u0026thinsp;0.88; Rank\u0026thinsp;=\u0026thinsp;4th). The Taguchi initial optimal parameter combination for raising the hardness depends on the larger-the-better characteristic of dissimilar automated MIG-welded SS316 stainless steel and AISI 4140 low alloy steel is A3B1C1D3.\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\u003eResponse table for signal-to-noise ratios for Hardness\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLevel\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWelding\u003c/p\u003e \u003cp\u003eCurrent (A)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eWire Feed\u003c/p\u003e \u003cp\u003eRate (B)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eGas flow\u003c/p\u003e \u003cp\u003eRate (C)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eWelding\u003c/p\u003e \u003cp\u003eSpeed (D)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e53.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e54.62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e54.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e53.33\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e53.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e53.82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e53.96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e53.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e54.71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e53.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e53.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e54.88\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDelta\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.55\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRank\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1\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 main impact plot graph and analysis for the S/N ratios indicate that level 3 current is the best value for maximizing hardness. (140A), wire feed rate at level 1 (7 cm/s), gas flow rate at level 1 (7 CFH), and welding speed at level 3 (40 cm/min). As the current rises from level 1(one) to level 3(three), the hardness value is rises. The hardness of the material decreases as the wire feed rate increases from level 1(one) to 3(three). As the gas flow rate increased from level 1(one) to 3(three), the hardness value is decreased. The welding speed was the most important welding factor, when welding speed rises from level one to level three the hardness value is increased discovered the same result for weld in AISI 4140 Steel.\u003c/p\u003e \u003cdiv id=\"Sec23\" class=\"Section3\"\u003e \u003ch2\u003e5.3.1 Analysis of Variance (ANOVA) for hardness\u003c/h2\u003e \u003cp\u003eThe ANOVA was developed for hardness to investigate the developed experiment was adequate or not. The most important qualities (most important parameters) that affect the automated MIG-welded steels' hardness are found and showed in Table\u0026nbsp;14 using analysis of variance (ANOVA). Research was undertaken utilizing their S/N ratios to establish the percentage contribution of each component. All of the factors had a very significant impact on hardness because the F-value in the ANOVA table 14 was more than the F-critical. Hardness of automated MIG-welded SS 316 stainless-steel with AISI 4140 low alloy steel with a thickness of 3 mm was significantly affected by the welding speed percentage (35.56%), welding current (31.24%), wire feed rate (18.68%), and gas flow rate (10.64%) as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig17\" class=\"InternalRef\"\u003e17\u003c/span\u003e below is the Percentage contribution of parameters for hardness. The ANOVA analysis, including p-values, indicates that the percentage of contribution from all parameters significantly impacts the hardness (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05). The R-squared value, which measures the model's fit to the data, is 96.11% indicating a strong fit.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe errors in statistical models are believed to be regularly distributed in the design of experiments. A normal probability plot of the residuals is used to evaluate the importance of effects. If the residuals fall along a straight line on the plot, they have a normal distribution. The data points are pretty near to the fitted normal distribution line. The residuals' normal probability plots reveal that they are pretty near to the normal probability lines. Furthermore, the p-value (0.105) is larger than 0.05, indicating that the residuals are normally distributed and that the variables in the regression models are both significant and appropriate. The maximum bending force was obtained via a four -point bend test performed on a universal testing machine, and flexural strength was calculated using this value and cross-section area. Table\u0026nbsp;\u003cspan refid=\"Tab7\" class=\"InternalRef\"\u003e7\u003c/span\u003e displays the mean flexural strength values measured for each trial experiment.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab7\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 7\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eFlexural strength test result\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=\"char\" char=\".\" 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=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWeld Current (A)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eWire feed Rate (B)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eGas flow Rate\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eWeld speed (D)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eFlexural strength\u003c/p\u003e \u003cp\u003e(MPa)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eSignal to noise ratio\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e110\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e788\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e57.9305\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e110\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e556\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e54.9015\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e110\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e455\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e53.1602\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e125\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e698\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e56.8771\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e125\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e766\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e57.6845\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e125\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e727\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e57.2307\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e140\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e831\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e58.3920\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e140\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e756\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e57.5704\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e140\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e811\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e58.1804\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=\"Tab8\" class=\"InternalRef\"\u003e8\u003c/span\u003e reveals that the highest value is for welding current (A) at level 3, followed by welding speed (D) at level 1, wire feed rate (B) at level 1, and gas flow rate (C) at level 1. The A3B1C1D1 welding settings were selected as the initial parametric configuration for MIG welding stainless steel 316 to AISI 4140 low alloy steel, which has a 3 mm thickness.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab8\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 8\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eResponse table for means of Flexural Strengths\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLevel\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWelding\u003c/p\u003e \u003cp\u003eCurrent (A)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eWire Feed\u003c/p\u003e \u003cp\u003eRate (B)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eGas Flow\u003c/p\u003e \u003cp\u003eRate (C)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eWelding\u003c/p\u003e \u003cp\u003eSpeed (D)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e599.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e772.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e757\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e788.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e730.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e692.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e688.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e704.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e799.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e664.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e684\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e636.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDelta\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e199.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e108\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e152\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRank\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\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2\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 most important element is welding current weight% (Delta\u0026thinsp;=\u0026thinsp;2.72; Rank 1st), followed by welding speed (Delta\u0026thinsp;=\u0026thinsp;2.06; Rank\u0026thinsp;=\u0026thinsp;2nd) and wire feed rate (Delta\u0026thinsp;=\u0026thinsp;1.54; Rank\u0026thinsp;=\u0026thinsp;3rd). The (S/N) ratio is least affected by gas flow rate (Delta\u0026thinsp;=\u0026thinsp;1.16; Rank\u0026thinsp;=\u0026thinsp;4th) according to the experimental result. The initial ideal parameter combination for increasing the flexural strength of MIG-welded 316 stainless steel with AISI 4140 low alloy steel sheets was A3B1C1D1 based on the larger-the-better flexural strength characteristic.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec24\" class=\"Section2\"\u003e \u003ch2\u003e5.4 Effect of process parameters on flexural strength\u003c/h2\u003e \u003cp\u003eThe influential welding factors on flexural strength are shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig18\" class=\"InternalRef\"\u003e18\u003c/span\u003e, It depicts the mean ranking values in each sets The primary effect plot graph and analysis for the S/N ratios indicate the ideal values for optimizing flexural strength, are current at level three (140A), wire feed rate at level one (7cm/s), gas flow rate at level one (7 CFH), and welding speed at level one (20cm/min), with numerical parametric setting of A3B1C1D1. The material's flexural strength decreases as the wire feed rate rises from 1st level to 3rd level. The other element was the gas flow rate; as the gas flow rate increased from 1(one) to 3(three) the flexural strength reduced dramatically. The second most critical welding attribute was the welding speed; as welding speed rises from 1st level to 3rd level the bending strength decreases observed the same investigation as welding speed rises from level one to level two flexural strength increase. Table\u0026nbsp;18 shows how ANOVA was used to evaluate the appropriateness of the developed models. This technique indicates that the model is insufficient within the confidence limit because, at the intended 95% level of confidence, the calculated F-ratio value of the came up with was lower than the typical F-ratio (F-table value 5.32). The experimental results show that since F-critical\u0026thinsp;=\u0026thinsp;5.32 is smaller than the F-ratio, all parameters significantly impacted flexural strength. The welding current (47.71%), welding speed (27.65%), and wire feed rate (13.96%) have a highly significant impact, The gas flow rate (6.38%) has a major effect on the bending strength of MIG-welded 316-Stainless Steel with AISI 4140 low alloy steel plates.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe errors in statistical models are believed to be regularly distributed in the design of experiments. Furthermore, the p-value (0.065) is larger than 0.05, indicating that the residuals are appropriately distributed and that the variables in the regression model are significant.\u003c/p\u003e \u003c/div\u003e"},{"header":"6. Conclusion and Recommendation","content":"\u003cp\u003eThis study was investigated and optimized the factors of automated MIG welding concerning to non-similar metals (SS316 and AISI4140) analyzed using a Taguchi L9 orthogonal array and the signal-to-noise ratio method, and ANOVA was used to determine the influence of each parameter. Tensile strength of 636MPa was recorded by parametrically combining 140A welding current, 7cm/s wire feed rate, 11CFH gas flow rate, and 30cm/s welding speed. According to ANOVA, welding speed and current were the two factors that had the greatest impact on tensile strength. 140A welding current, 10.5cm/s wire feed rate, 7CFH gas flow rate, and 40cm/s welding speed were used in a parametric configuration to produce a maximum hardness of 625HV. ANOVA revealed that welding current was the second-most significant factor for hardness after welding speed. An experimental setup of 140A welding current, 7cm/s wire feed rate, 11CFH gas flow rate, and 30cm/min welding speed resulted in a maximum flexural strength of 692MPa. Welding current, followed by welding speed, was found to be the most crucial component in flexural strength by analysis of variance (ANOVA). To achieve a significant weld width of 6.21 mm, a combination of 125A welding current, 7cm/s wire feed rate, 9CFH gas flow rate, and 40cm/min welding speed was employed. An analysis of variance (ANOVA) it was observed that welding current, followed by welding speed, was the factor that had the greatest impact on weld width. The following recommendation and scope for future work are made based on the study\u0026rsquo;s findings and conclusion. Particularly in relation to the joint interface area, and application of heat treatment to improve the properties of welded joints produced by automated MIG welding, particularly those involving dissimilar metals such as SS 316 and AISI 4140.Automated MIG welding of SS 316 with AISI 4140 was employed in this study, and it can be extended to other dissimilar materials with varying thickness, Futhermore HAZ and mechanical characterisation with reinforcement can be explored by applying Automated MIG approach.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics declarations\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eEthical approval\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003eConsent to participate\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003eConsent to publish\u003c/p\u003e\n\u003cp\u003eAll authors have read and approved this manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contributions:\u003c/strong\u003e Conceptualization T.A.D, H.B; Methodology, T.A.D, H.B and A.A.R.; software, S.Z.K , T.A.D, H.B and A.A.R \u0026nbsp;and G.M.S.A; validation, T.A.D, H.B and G.M.S.A; formal analysis, S.Z.K, A.A.R, A.E, A.A.D and G.M.S.A.; investigation T.A.D, H.B.; resources, S.Z.K, H.B, A.A.R,A.E and G.M.S.A.; writing\u0026mdash;original draft preparation, T.A.D and H.B; writing\u0026mdash;review and editing, T.A.D, H.B, A.A.D, A.E and G.M.S.A.; visualization, S.Z.K , A.A.R, A.A.D, A.E and G.M.S.A.; supervision, H.B; project administration, S.Z.K, A.A.R, A.E \u0026nbsp;and G.M.S.A; funding acquisition, A.E \u0026nbsp;All authors have read and agreed to the published version of the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data that support the findings of this study are available within the manuscript.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003cstrong\u003eDeclaration of Competing Interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe authors extend their appreciation to the Deanship of Scientific Research at King Khalid University for funding this work through the large group Research Project under grant number RGP2/415/44.\u003c/p\u003e\u003cp\u003eConflicts of Interest: The authors declare no conflict of interest.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAkinlabi, S. 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World Academy of Science, Engineering and Technology, 39(March), 280\u0026ndash;284.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Automated MIG Welding, Multi-Response Optimization Mechanical Properties, ANOVA, Flexural Strength","lastPublishedDoi":"10.21203/rs.3.rs-3854720/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3854720/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eIn marine, aerospace, automotive, and biomedical engineering, components and structures are made of different materials that have properties in order to meet specific performance requirements. Stainless steel 316 and low alloy steels are the most commonly used materials in the marine industry, where corrosive resistivity and high specific strength are the design requirements. This was the main importance of welding dissimilar metal using automated MIG to increase productivity, improve quality, reliability, and reduce labor costs. This research work aimed to study how different MIG process parameters (welding current, wire feed rate, gas flow rate, and welding speed) affect the mechanical properties of welding two different metals together, namely stainless steel 316 and low alloy steel 4140. To study the significance and contribution of each parameter, analysis of variance (ANOVA) were conducted using Minitab 21 software. The tensile strength and flexural strength experimental results showed that the welding current parameter has a high significant effect in both cases, followed by welding speed. The hardness result shows that the welding speed followed by the welding current has a significant effect on hardness. Weld metal had higher hardness than stainless steel and low alloy steel base metals.\u003c/p\u003e","manuscriptTitle":"Experimental Investigation and Parametric Optimization of Automated MIG welding Stainless Steel SS316 with Low Alloy Steel AISI 4140 on Mechanical Properties","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-01-17 17:19:15","doi":"10.21203/rs.3.rs-3854720/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"540a6462-5eb4-4815-8be4-d950fec657f9","owner":[],"postedDate":"January 17th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-04-24T13:43:09+00:00","versionOfRecord":[],"versionCreatedAt":"2024-01-17 17:19:15","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-3854720","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3854720","identity":"rs-3854720","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}