Multi-response Optimisation of Wire-EDM for SLMed AlSi10Mg using Taguchi-Grey Relational Theory

Multi-response Optimisation of Wire-EDM for SLMed AlSi10Mg using Taguchi-Grey Relational Theory | 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 Multi-response Optimisation of Wire-EDM for SLMed AlSi10Mg using Taguchi-Grey Relational Theory Murali Krishnan R, Rajesh Ranganathan, Saiyathibrahim A, Rajkumar Velu, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4494311/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 The present research effort strives to optimise the multi-response during Wire Electrical Discharge Machining (Wire-EDM) of SLMed AlSi10Mg, applying Taguchi integrated Grey Relational Analysis (GRA). Selective Laser Melting (SLM) represents one of the best-known and most practicable Additive Manufacturing (AM) methods that have the prospective to serve as a replacement for many traditional production processes. Extremely intricate metallic support structures built up during SLM need more attention since they are too difficult to remove by hand. Therefore, post-processing adopting the Wire-EDM precision machining technique is performed in this study to assess the machinability of the SLMed AlSi10Mg as-built part. The multi-response optimisation used here seeks to achieve maximum material removal rate and lowest surface roughness while considering four important influencing elements (pulse On time, pulse Off time, servo voltage, and wire feed rate) at four distinct levels. Taguchi integrated Grey Relational Analysis (GRA) revealed that a pulse On time of 118 µs (Level 3), a pulse Off time of 44 µs (Level 1), a servo voltage of 60 V (Level 4), and a wire feed rate of 7 m/s (Level 4) are suggested to achieve optimal machining of SLMed AlSi10Mg. Furthermore, the derived optimisation results were diligently verified using a confirmatory experiment, and a 38.57% improvement in multi-response characteristics was found when compared to the initial Wire-EDM parameter settings. The methodology suggested in this work offers a standard approach that has the potential to be implemented for the rapid and precise prediction and optimisation of surface roughness while achieving better material removal during Wire-EDM of SLMed AlSi10Mg. SLM AlSi10Mg Wire-EDM Taguchi orthogonal array Grey relational analysis (GRA) SEM Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 1. Introduction Additive Manufacturing (AM) allows the production of components incorporating a high degree of complexity that traditional subtractive manufacturing cannot achieve [ 1 ]. Metal additive manufacturing has been gaining significant interest in both research and industrial sectors because of its ease of producing intricate components [ 2 ]. Selective laser melting (SLM) is one of the types of metal AM that is highly advantageous and belongs to the category of powder bed fusion (PBF). It can produce complicated metal components layer-by-layer using digital models with maximum flexibility and greater accuracy, reducing processing cycles and material waste [ 3 ]. Aluminium alloys (AlSi12, AlSi12Mg, AlSi10Mg), different types of steels (SS 316L, hot-work steel, tool steel, maraging steel), titanium alloys (Ti6Al4V, Ti6Al7Nb), Inconel 718, Inconel 625, cobalt alloys, gold, copper, etc. are among the popular materials processed by SLM [ 4 ]. In particular, the demand for aluminium alloys has increased due to their greater utilisation in producing superior-quality commercial goods, which suggests that research into their functionality has become a top priority for researchers [ 5 ]. AlSi10Mg is a nearly eutectic, castable alloy that finds high demand in the aviation and automobile sectors due to its low weight, minimal thermal expansion, and excellent mechanical characteristics [ 6 ]. Processing intricate AlSi10Mg parts through SLM seems beneficial because it results in sound as-built structures with good dimensional stability. The mechanical and surficial properties of as-built AlSi10Mg parts are connected to utilising optimised SLM process parameters and employing post-processing routes. Synthesising pore-free, dense, stable SLMed AlSi10Mg has been thoroughly investigated with several optimised operating parameters [ 7 ]. In recent years, a significant amount of research has been carried out towards the surface quality of SLMed AlSi10Mg products. It is well known that good surface quality is always an expectation in any manufacturing process, and poor surface characteristics induce cracks in parts [ 8 ]. A complex part is built in the SLM route using many support structures holding overhanging areas to obtain structural stability. These are to be removed through post-processing techniques without creating damage to the build part/component [ 9 , 10 ]. However, cyclic layer-by-layer application of material and fusion process results in poor surface characteristics in components compared to parts made from conventional manufacturing routes [ 11 ]. The most common issue in metal AM techniques is surface irregularities exhibiting poor morphology and randomly oriented features formed due to continual layer-by-layer deposition and fusion [ 12 , 13 ]. A careful investigation of surface characteristics of as-built AM parts is most required since such surface features significantly influence effective performance. Hence, improving surface quality is necessary to oppose the establishment of mechanical failures of AM parts that are extremely dependent on surface properties [ 14 , 15 ]. Recently, some surface post-treatments have been proposed by researchers aiming to impart good surface quality in metal AM parts [ 16 ]. Boban & Ahmed [ 17 ] proposed a novel EDIP (Electric Discharge Internal Processing) technique to post-process holes of AlSi10Mg blocks printed using LPBF (Laser Powder Bed Fusion) and suggested that this adopted technique enhances surface finish by removing internal surface irregularities. Atzeni et al. [ 18 ] employed the Abrasive Fluidized Bed (AFB) route to enhance the surface quality of AlSi10Mg build part printed using Direct Metal Laser Sintering (DMLS), and the observed results showed that shorter operational cycles are recommended to produce good surfaces and there are again surface damages created due to impact of abrasive particles at the edges. Peng et al. [ 19 ] investigated the surface finish and compressive stress behaviour of additively manufactured AlSi10Mg using Abrasive Flow Machining (AFM) and showed effectiveness in improving the surface integrity of the as-built part. Teng et al. [ 20 ] combined the conventional grinding process and Magnetic Abrasive Finishing (MAF) to minimise surface roughness and enhance the surface quality of the SLMed AlSi10Mg part, and the results showed it to be beneficial. A similar response was identified when using disk laser-aided heat treatments combined with glass bead blasting during surface quality analysis SLMed AlSi10Mg [ 21 ]. It was also identified during the chemical polishing of this same material in another investigation that surface finishes largely improved by around 80% [ 22 ]. Schneller et al. [ 23 ] assessed the effect of surface quality on the fatigue property of SLMed AlSi10Mg and found an increase in surface quality of around 60% when employing Hot Isostatic Pressing (HIP). From the above discussion, it can be understood that only limited research has been carried out so far in this field. In addition, limited techniques are only considered to investigate surface characteristics of the SLMed AlSi10Mg part. Vaidyaa et al. [ 24 ] optimised Wire-EDM parameters for better surface characteristics of SLMed AlSi10Mg part employing Genetic Algorithm (GA) coupled Artificial Neural Network (ANN), through which 15% of improvement was attained. It is well known that Wire-EDM has been showing better machining performance since its inception, and precise quality machining can be conducted effectively using this process [ 25 ]. Since AM parts are largely employed in aerospace and automotive sectors, higher surface finish is always mandatory, which can be achieved by efficiently implementing Wire-EDM to remove support structures and uneven surfaces created during layer-by-layer processing of AM. In addition, it is a non-contact advanced machining process, resulting in less thermal stress on the machined parts. Further, optimising process parameters results in better outcomes with shorter manufacturing time [ 26 ]. Consequently, it is appropriate to implement this advantageous approach to increase the surface quality of SLM printed parts. Previous literature indicates significant potential for enhancing the surface properties of SLMed AlSi10Mg by utilising Wire-EDM and implementing multi-objective optimisation. Hence, this work aims to identify the most effective combination of machining parameters that may yield a greater material removal rate while minimising surface roughness. This will be accomplished using Taguchi integrated Grey Relational Analysis (GRA), a strategy that, to the best of our knowledge, has not been previously described. In this study, the AlSi10Mg part was built using SLM and its microstructural variations were observed. To obtain a better surface finish on the SLMed AlSi10Mg part, Wire-EDM parameters were optimised using Taguchi-grey relational theory. In addition to surface characteristics, the rate of material removal during Wire-EDM was also included for investigation since lesser processing time resulting in higher material removal is always beneficial. SEM morphology of machined surfaces was discussed elaborately to identify surface features. 2. Optimisation Procedure Machining of additively fabricated AlSi10Mg specimen using Wire-EDM involves a number of performance characteristics, with Material Removal Rate (MRR) being a higher-the-better performance characteristic and Surface Roughness (SR) having the lower-the-better characteristic. Therefore, using Taguchi-based single response optimisation may improve one performance attribute at the expense of another. To resolve this issue, combining GRA with the Taguchi technique has been successfully reported to be beneficial in dealing with processes that incorporate several performance characteristics [ 27 ]. 2.1. Taguchi method Taguchi's orthogonal array-based Design of Experiments (DOE) is an effective method for defining the ideal combination of process parameters with no repetitions. This method not only saves time and clearly specifies how to efficiently carry out the required number of trials, but it also provides complete information on all of the machining variables that affect the response variables economically. The core function of this orthogonal array method is to identify the influential process variables and their levels. Taguchi discovered three loss functions for qualitative analysis of real-time engineering applications that are highly effective for predicting the deviations of response parameters from their goal values. They are, Smaller the better – The best outcome is a lower value Larger the better – The best outcome is a higher value Nominal is the best – The best result is mean value: between higher and lower values The Taguchi technique can generate an orthogonal array with fewer experiments using a set of input machining parameters and their pre-determined levels, avoiding experiment redundancy [ 28 ]. 2.2. Grey Relational Analysis As mentioned earlier, multi-response optimisation was carried out in this study utilising a Taguchi orthogonal array coupled with grey relational analysis (GRA). GRA is a decision-making technique based on Deng's grey system theory. The intention of GRA is to assess the degree of compactness and resemblance across sequences based on geometric shape. The grey relational grade quantifies the degree of link between response and reference sequences. The steps for performing GRA are as follows [ 29 ]: 2.2.1. Normalising the results obtained from experiments The response parameters MRR and SR were normalised linearly within a range of 0 to 1 using a technique referred to as grey relational generation. The outcomes of normalising can be represented as, For larger the better, $${X}_{h}\left(P\right)=\frac{{X}_{h}^{^\circ }\left(p\right) - min{X}_{j}^{^\circ }\left(p\right) }{max{X}_{j}^{^\circ }\left(s\right) - min{X}_{j}^{^\circ }\left(s\right) }$$ 1 For smaller the better, $${X}_{h}\left(P\right)=\frac{max{X}_{h}^{^\circ }\left(p\right) - {X}_{h}^{^\circ }\left(p\right) }{max{X}_{h}^{^\circ }\left(p\right) - min{X}_{h}^{^\circ }\left(p\right) }$$ 2 where, \({X}_{h}\left(P\right)\) is the normalised value, \({\text{X}}_{\text{h}}^{\text{°}}\text{(p)}\) is the original value, \(\text{m}\text{a}\text{x}{\text{ X}}_{\text{h}}^{\text{°}}\text{(p) }\) is the largest value among the original values and \(\text{m}\text{i}\text{n}{\text{ X}}_{\text{h}}^{\text{°}}\text{(p)}\) is the smallest value among the original values. 2.2.2. Calculating the Grey Relational Coefficients (GRC) After normalisation, the deviation sequence for each experiment must be computed. It is the difference between the reference and original sequences, which can be described as, $${\varDelta }_{mn}\left(P\right)=\left|{X}_{m}\left(p\right)-{X}_{h}\left(p\right)\right|$$ 3 where, \({\varDelta }_{mn}\left(P\right)\) is the deviation sequence, and \({X}_{m}\left(p\right)\) is the reference sequence. These grey relational coefficients have been determined to illustrate the relationship between the experimental observations and ideal or better values. Grey Relational Coefficient can be presented as, $${\xi }_{h}\left(P\right)=\frac{{\varDelta }_{min}+\xi {\varDelta }_{max}}{{\varDelta }_{mn}\left(P\right)+\xi {\varDelta }_{max}}$$ 4 where \(\xi\) is the identification coefficient varies from 0 to 1 and \({\text{∆}}_{\text{min}}\) as well as \({\text{∆}}_{\text{max}}\) are the minimum and maximum deviations of each response parameter, respectively. 2.2.3. Calculating the Grey Relational Grades (GRG) In order to illustrate the closeness of each experimental outcome produced to the ideal value, grey relational grades were computed. A higher GRG in an experimental result indicates that the finding is extremely significant. The grey relational grade is used to assess the overall quality of multiple responses. Consequently, optimising complicated multiple process outcomes may be reduced to a single grey relational grade. In simpler terms, GRG may be viewed as a thorough examination of data from experiments for the multi-response method. The grey relational grade ( \({\gamma }_{h}\) ) for any experiment was calculated using the following Eq. ( 5 ), $${\gamma }_{h}=\frac{1}{n}\sum _{h=1}^{n}{\xi }_{h}\left(P\right)$$ 5 2.2.4. Analysis of Variance (ANOVA) ANOVA is a statistical tool used to determine the effects of all input machining variables on the results. The primary objective of ANOVA is to use the grey relational grades obtained to classify the most significant machining parameter with its proportion of contribution for a specific response [ 30 ]. 2.2.5. Confirmation test Finally, a confirmation test has been carried out to assess the dependability of multi-response optimisation utilising the optimal input machining parameters determined by grey relational grading. In order to produce the desired response, optimum machining parameters had been selected throughout this test, and the level of confidence was considered to be 90 to 95%. 3. Materials and Methods 3.1. Fabrication of SLMed AlSi10Mg AlSi10Mg specimen with an approximate size of 50 x 50 x 10 mm was developed using the Selective Laser Melting (SLM) technique on an EOS M290 machine. The machine is provided with a building platform with dimensions of 250 × 250 × 325 mm 3 . The printing chamber operates in an inert atmosphere (Argon), with a laser power of 400 W and a focus diameter of 100 µm. The powder was continually supplied by the system with a dispenser unit supplying AlSi10Mg with 40 µm throughout the printing process. Table 1 shows the powder composition of AlSi10Mg and selected SLM process variables, which ensured the fabrication of fully dense components. The sample was manufactured horizontally (parallel to the powder bed X-Y plane) using selected parameters listed in Table 2 . Table 1 AlSi10Mg chemical composition Element Si Cu Fe Ni Zn Mg Mn Pb Sn Ti Al wt% 9.8 0.05 0.55 0.05 0.10 0.35 0.45 0.05 0.05 0.15 Balance Table 2 SLM parameters employed for printing Specification/Process parameter Unit Type/Value Laser beam spot size µm 100 Atmosphere - Argon Laser power W 370 Layer scan speed mm/s 1300 Layer thickness µm 30 Hatch spacing mm 0.19 Build platform temperature o C 35 Volume rate mm 3 /s 5.1 3.2. Wire-EDM of SLMed AlSi10Mg Wire-EDM is a precision manufacturing process that uses a thin wire as a tool electrode to establish an electro-thermal material removal mechanism. In this approach, the machining zone is flooded with dielectric fluid. This fluid serves as an insulator, and the material is removed from the workpiece by a series of electrical discharges. In detail, the advancement of the wire towards the workpiece creates a gap in which a substantial voltage is created, which breaks the dielectric fluid, causing an electrical discharge and, therefore spark erosion material removal. For a precise cut, the procedure outlined above is repeated approximately 2,40,000 times per second. In addition to cooling, dielectric fluid flushes all machining byproducts from the machining gap [ 31 ]. Figure 1 depicts the process of machining the SLMed AlSi10Mg part using the Wire-EDM process. The experimental runs of the SLMed AlSi10Mg part were performed on an Electronica Sprintcut Win Plus CNC Wire-EDM (five-axis) machine, and its detailed features are listed in Table 3 . A 0.25 mm diameter brass (copper 60% + zinc 40%) wire electrode submerged in dielectric fluid was employed in this Wire-EDM process. Table 3 Specifications of Wire-EDM machine used Description Specification Model Electronica - Sprintcut Win Plus Material removal mechanism Melting and evaporation Worktable size 440 x 650 mm Main axes traverse (X, Y) 300 x 400 mm Aux. axes traverse (u, v) 80 x 80 mm Max. workpiece height 250 mm Max. taper angle ± 30˚/50 mm Resolution 0.0005 mm Max. JOG speed 900 mm/min Max. wire spool capacity 6 kg (up to DIN 160 / P5) Dielectric fluid Deionised water Max. workpiece weight 300 kg Wire electrode Brass wire of 0.25 mm diameter The investigation was performed employing an L16 orthogonal array. Four key factors-pulse On time (A), pulse Off time (B), servo voltage (C), and wire feed rate (D)-were considered in order to transform the design into a four-level, four-factorial Taguchi design. Minitab 19 version software was used to develop this experimental design. The details of Wire-EDM parameters and their levels involved in framing the L 16 orthogonal array are depicted in Table 4 . Figure 1 shows the Wire-EDM machine used for machining, machining process, and machined as-built AlSi10Mg part. Table 5 shows the experimental runs generated by integrating machining parameters of various levels and the responses observed. Table 4 Wire-EDM parameters and their levels Parameters Units Levels Level 1 Level 2 Level 3 Level 4 Pulse On Time (A) µs 114 116 118 120 Pulse Off Time (B) µs 44 45 46 47 Servo Voltage (C) V 30 40 50 60 Wire Feed Rate (D) m/s 4 5 6 7 Table 5 L16 orthogonal array with observed responses Exp. Run Wire-EDM parameters Response parameters Pulse On Time (A) Pulse Off Time (B) Servo Voltage (C) Wire Feed Rate (D) Material Removal Rate (MRR) Surface Roughness (SR) (µs) (µs) (V) (m/s) (g/min) (µm) 1 114 44 30 4 0.05354 1.429 2 114 45 40 5 0.20650 2.613 3 114 46 50 6 0.09501 3.141 4 114 47 60 7 0.32505 2.640 5 116 44 40 6 0.15296 3.135 6 116 45 30 7 0.34608 2.041 7 116 46 60 4 0.30593 2.219 8 116 47 50 5 0.24665 3.378 9 118 44 50 7 0.32505 1.755 10 118 45 60 6 0.14723 2.224 11 118 46 30 5 0.16004 2.019 12 118 47 40 4 0.04589 1.697 13 120 44 60 5 0.10325 1.820 14 120 45 50 4 0.22885 3.548 15 120 46 40 7 0.26004 3.463 16 120 47 30 6 0.13002 3.629 3.3. Microstructural observation 3.3.1. Surface morphology Surface morphology plays an essential role in assessing the quality of machined surfaces. Furthermore, printed specimens in their as-built form, as well as as-cast specimens, are evaluated by utilising an optical microscope (Carl Zeiss Axioscope 5) and Scanning Electron Microscope (SEM) with Energy Dispersive X-ray spectroscopy (EDX) (JSM-IT200 InTouchScope). All microstructural samples were prepared by hand polishing with various grades of emery sheets, followed by disc polishing, and finally, an etching process using Keller's reagent of 95 ml distilled water, 2.5 ml HNO 3 , 1.5 ml HCl, and 1 ml HF for 30 seconds to disclose the microstructure. In addition, 3.3.2. X-ray diffraction (XRD) To assess the phase composition, X-ray diffraction was carried out on an as-built SLMed AlSi10Mg sample using a continuous scan mode of 10°/min utilising an Empyrean Series III Diffractometer having phase composition shifted from 20° to 90° and employing X-ray source of Cu Kα (λ = 1.54 Å) anticathode at 35 kV and 40 mA. 3.4. Measurement of Surface Roughness (SR) Surface roughness can be expressed as a variation of the actual surface measured along the normal vector direction from the ideal surface value. A rough surface generates a lot of deviation, whereas a smooth one produces very minimal [ 30 ]. The surface roughness of a machined specimen is thought to have a significant influence on its mechanical properties. This inquiry examines it by utilising a Mitutoyo SJ-210 surface roughness tester. Surface roughness values were measured three times at each machined region, and the average of these observations was adopted as the final value [ 32 ]. 3.5. Measurement of Material Removal Rate (MRR) Material Removal Rate constitutes one of the most essential response variables to consider while machining a component/part. It has a substantial relationship with product quality, production rate, and manufacturing efficiency. MRR is the rate at which material is removed on a manufactured part in a specific duration [ 33 ]. MRR has been calculated for each experimental run using the weight loss technique during machining, which is displayed in Eq. ( 6 ). After finishing each experimental run, a digital weighing balance (accuracy = 0.001 g) was utilised to quantify the before and after weight of the machining specimen. A digital weighing balance (accuracy = 0.001 g) was used to measure the initial \({ (W}_{i})\) and final \({ (W}_{f})\) weight of machining specimen after completing each experimental run [ 34 ]. $$MRR=\frac{{W}_{i}\times {W}_{f}}{t} (\text{g}/\text{m}\text{i}\text{n})$$ 6 4. Results and Discussions 4.1. Microstructural observation of SLMed AlSi10Mg as-built specimen The optical microstructure of the SLMed AlSi10Mg printed part is shown in Fig. 2 a). The interfacial microstructure features created by pulsating laser beam motion consist of stretched teardrops from molten pool microstructure as well as melting boundary layers generated through continual wave laser beam movement. There is an evident multilayered structure and elliptical molten pools overlaying each other along the line of laser movement [ 35 ]. Specifically, the microstructure is composed of superimposed melt pools produced by sequential laser rastering, leading to the melting and solidifying of consecutive powder layers. An exceptional fine dendritic structure of the α-Al matrix governed by the eutectic Si phase arrived within the melt pools. The rapid cooling rates employed in the SLM technique resulted in the formation of an extremely fine structure. The growth of a hierarchical microstructure in SLMed AlSi10Mg is an essential characteristic resulting from distinct solidification conditions during the SLM process [ 36 ]. For comparison, an as-cast AlSi10Mg sample was additionally synthesised by gravity casting, and its microstructure was examined using SEM/EDX (Fig. 2 b)), and it differs significantly from the part printed by SLM. The processing technique clearly has a significant influence on microstructures and as-cast samples consisting of eutectic silicon and primary Mg 2 Si intermetallic phase in aluminium matrix, as proven by EDX observation (Fig. 2 b)). It should also be mentioned that the solid solution of silicon in aluminium dissolves and precipitates in a considerably coarser form due to the slower cooling rate employed in a typical AlSi10Mg gravity casting procedure [ 37 ]. As mentioned earlier, typical α-Al phase and eutectic Si were observed in the SLMed AlSi10Mg sample. The rapid cooling nature of the SLM process enables Si to become supersaturated and precipitate in the Al matrix [ 38 ]. Owing to the continual exposure of the printing part to heating cycles during the SLM process, Mg and Si atoms diffused into the supersaturated α-Al matrix and formed Mg 2 Si precipitates. These precipitates are believed to enhance strength by minimising dislocation motion in the printed part [ 36 ]. Furthermore, both microstructures reveal no porosity problems, indicating that excellent experimentation was carried out. The XRD spectra of the SLMed as-built AlSi10Mg sample are shown in Fig. 3 , and it demonstrates a very low intensity of Si peaks due to less silicon (9.8 wt%) in the examined material compared to aluminium, suggesting Mg 2 Si establishment [ 39 ]. 4.2. Multi-response Optimization using Grey relational Analysis (GRA) The Wire-EDM behaviour of SLMed AlSi10Mg was satisfactorily examined, and Table 6 shows the Grey Relational Grades (GRGs) generated by pre-processed responses. Because of the heterogeneity in measurement units, all recorded original responses were normalised using Equations 1 and 2 during pre-processing and transformed into values ranging from 0 to 1. The normalised response values and their associated deviation sequences are shown in Table 6 . The deviation sequences were generated using Eq. 3 and then utilised to determine the relevant grey relational coefficients and grey relational grades using Equations 4 and 5 . The identification coefficient ( \(\xi\) ) was set to 0.5 as per the standard approach to ensure equal weightage throughout the calculation of grey relational coefficients for each response. Abegunde et al. [ 40 ] suggested choosing a higher grey relational grade to obtain more effective multi-response characteristics. Furthermore, the ideal combination of machining parameters is often indicated by the higher grey relational grade assigned to rank 1 [ 41 ]. The ranking of each experiment and the values of grey relational grades are displayed in Table 6 . Table 6 Grey Relational Grades obtained from pre-processing of responses Exp. Run Pre-processed responses (Ideal = 1) Grey Relational Coefficient (GRC) (Ideal = 1) Grey Relational Grade (GRG) Order MRR SR MRR SR 1 0.025478 1 0.339093 1 0.6695 5 2 0.535032 0.461818 0.518152 0.481611 0.4999 10 3 0.163631 0.221818 0.374148 0.391181 0.3827 15 4 0.929936 0.449545 0.877095 0.475984 0.6765 4 5 0.356688 0.224545 0.437326 0.392017 0.4147 14 6 1 0.721818 1 0.642523 0.8213 2 7 0.866242 0.640909 0.788945 0.582011 0.6855 3 8 0.66879 0.114091 0.601533 0.360774 0.4812 12 9 0.929936 0.851818 0.877095 0.771388 0.8242 1 10 0.33758 0.638636 0.430137 0.580475 0.5053 9 11 0.380255 0.731818 0.44653 0.650888 0.5487 8 12 0 0.878182 0.333333 0.804094 0.5687 6 13 0.191083 0.822273 0.381995 0.73776 0.5599 7 14 0.60949 0.036818 0.561476 0.341721 0.4516 13 15 0.713376 0.075455 0.635628 0.350989 0.4933 11 16 0.280255 0 0.409922 0.333333 0.3716 16 Analysis of Variance (ANOVA) has been carried out effectively in this investigation using Minitab 19 statistical software. Table 7 displays the impact of each process parameter on the basis of GRG at each level. It also displays the computed total mean of grey relational grades for all 16 experimental runs, which comes out to be 0.5597. A grey relational grade represents the degree of correlation between the reference sequence and the original sequence [ 42 ]. The original and reference sequences are more strongly correlated, as indicated by the higher value of the grey relational grade. Consequently, the level with the highest grey relational grade value is the ideal level of machining parameters. An improved Wire-EDM performance is achieved by the parameters indicated by the level value that is shown with an asterisk (*). The most effective machining performance for SR and MRR was identified for the combination of pulse On time (A) (level 3), pulse Off time (B) (level 1), servo voltage (C) (level 3), and wire feed rate (D) (level 4), based on the higher grey relational grade presented in Table 6 . Table 7 Grey Relational Grades obtained from pre-processing of responses Wire EDM parameters Levels Max - Min Level 1 Level 2 Level 3 Level 4 Pulse On Time (A) 0.5572 0.6006 0.6117* 0.4691 0.1426 Pulse Off Time (B) 0.6171* 0.5695 0.5275 0.5245 0.0926 Servo Voltage (C) 0.6028 0.4941 0.5349 0.6068* 0.1127 Wire Feed Rate (D) 0.5938 0.5224 0.4186 0.7038* 0.2853 Mean value of Grey Relational Grade = 0.5597 However, according to Table 7 , in the context with several performance characteristics, A3B1C4D4 is the ideal level of Wire-EDM parameters since a higher mean value for each factor provides better outcomes. Additionally, the variation between maximum and minimum values of grey relational grades has been estimated and tabulated in Table 7 . For the Wire-EDM parameters, the deviation between the maximum and minimal values of the average grey relational grade is as follows: 0.1426 for the pulse On time, 0.0926 for the pulse Off time, 0.1127 for the servo voltage, and 0.2853 for the wire feed rate. By comparing these data, the most essential parameter influencing machining performance will be identified. The greatest of these numbers was the most effective controllable factor. Considering this, the wire feed rate has been identified as the machining parameter with the most influence, as it had the highest GRG mean value of 0.2853 compared to all other parameters investigated in the current study. In addition, it is possible to estimate the significance of each machining parameter, aiming at multi-response characteristics of the printed specimen, by looking at the mean values of GRGs. Wire-EDM mean data values show the following influencing order: wire feed rate (D), pulse On time (A), servo voltage (C), and pulse Off time (B). As stated earlier, the wire feed rate has the most significant impact on the wire electrical discharge machining process for SLMed AlSi10Mg. Furthermore, as illustrated by the dashed line in the main effect plot graph (Fig. 4 ) constructed using estimated grey relational grades, the overall mean value obtained from this experimental investigation is 0.5597. In GRA, it is universally acknowledged that higher GRG mean values always represent more effective multi-response characteristics. The largest mean values, according to Table 7 , are A (Level 3), B (Level 1), C (Level 4), and D (Level 4). In other words, the ideal machining combinations determined by the GRG indicate that the response table is A3B1C4D4. The maximum values in each curve designated to each parameter are shown in Fig. 4 . These values are expected to improve Wire-EDM performance by producing low SR and high MRR. ANOVA is used to assess the influence of each parameter employed during Wire-EDM. The impact of each process parameter on the multi-response characteristics is effectively estimated through this statistical method [ 43 ]. Table 8 reveals the ANOVA results for the estimated GRGs of the Wire-EDM process. The percentage of contributions was determined for each machining parameter while accounting for error by processing the sum of squares values (variability between estimated GRG means). The percentage contribution calculated depending on the sum of squares values of process parameters during the Wire-EDM process of SLMed AlSi10Mg is shown in Fig. 5 . Furthermore, the parametric test (F test) and probability values (P value) were also performed [ 44 ]. Table 8 Results of ANOVA on grey grade Drilling parameters DF SS MS F-Value P-Value Contribution (%) Pulse On Time (A) 3 0.05040 0.016798 3.43 0.169 16.99 Pulse Off Time (B) 3 0.02265 0.007549 1.54 0.365 7.63 Servo Voltage (C) 3 0.03595 0.011982 2.45 0.241 12.12 Wire Feed Rate (D) 3 0.17300 0.057667 11.78 0.036 58.31 Error 3 0.01469 0.004897 4.95 Total 15 0.29668 The ANOVA results indicate that, when P values were computed, one of the four machining parameters under investigation had values less than 0.05. Due to this, the machining performance is greatly influenced by that one parameter (95% confidence level). The wire feed rate (D) has a more significant impact on the multi-response than the pulse On time (A), pulse Off time (B), and servo voltage (C), despite the fact that it is significant at the 95% confidence level. In summary, several parameters influenced the Wire-EDM of SLMed AlSi10Mg, and the details of each factor, along with their percentage contribution, are shown in Fig. 5 . Ramanujam et al. [ 45 ] identified that wire feed is the most influential parameter in collectively affecting surface roughness and material removal rate. According to Samal et al. [ 46 ], the wire feed rate, pulse On time, and servo voltage together substantially impact the MRR. These previous findings support the findings of this study, which show in Fig. 5 that the key essential parameters, including wire feed rate, should be at maximum (7 m/s), and it is ideal to have a pulse On time of 118 µs and servo voltage would be 60 V in order to attain better machining characteristics. Hence, it is clear that MRR was substantially improved by the increase in wire feed rate as well as higher pulse. On duration causes a rapid surge in thermal energy, which adds to higher workpiece erosion [ 47 ]. It also suggested from the understanding of the study that a higher wire feed rate generates smoother surfaces than an inferior wire feed rate. Because many craters may emerge in the very same area on the wire surface as long as the wire feed has been excessively low, which may result in wire breakages [ 48 ]. A higher pulse On time leads to a larger discharge energy, which elevates the amount of material removed. As a result, increasing the pulse duration, i.e., power and energy, elevates MRR [ 49 ]. The current study concludes that the impact of servo voltage and pulse Off time on producing lower surface roughness and a greater material removal rate is comparatively lower. Gupta et al. [ 50 ] revealed that the machining parameters such as servo voltage and wire feed substantially influence the machinability of the Wire-EDM process, and optimising these variables can result in better outcomes. The Normal probability plot based on the Grey Relational Grade is depicted in Fig. 6 . With the exception of one deviation, all points are adjacent to a straight line, illustrating that the computed grey grade exhibits a normal distribution. Wherever there is a steep slope in the plot, it can be concluded that those parameters have a considerable impact on the Wire-EDM process [ 51 ]. 4.3. Confirmation test By examining the GRGs of multi-response, optimal Wire-EDM parameters were identified. A confirmation test validated the reliability of this analysis. Following the selection of optimal machining settings, the next step is to compute response improvement by performance analysis. This important step validates the findings of the analytical phase [ 52 ]. Therefore, a confirmation experiment was performed to confirm this prediction. Wire electrical discharge machining was performed precisely with a pulse On time of 118 µs (Level 3), a pulse Off time of 44 µs (Level 1), a servo voltage of 60 V (Level 4) and a wire feed rate of 7 m/s (Level 4). Table 9 shows the confirmation test results using the optimal combination of Wire-EDM parameters. To better understand this multi-response optimisation study, the grey relational grade of the confirmatory experiment is compared with the reference experiment (Exp. Run 1 in Table 5 ). It shows that when the material removal rate increases from 0.05354 to 0.32140 g/min, the surface roughness is highly steady and increases slightly from 1.429 to 1.433 µm, which is comparably minor. For this multi-response research, the grey relational grade improved by 0.2582 units. As shown in Table 9 , there is a considerable improvement in GRG of 38.57%. The Taguchi technique, in connection with the GRA multi-response optimisation results, is satisfying and offers new insights into the machining capabilities of AlSi10Mg. Table 9 Confirmation test results Initial Optimal Wire EDM parameters Predicted Experimental Combination A1B1C1D1 A3B1C4D4 A3B1C4D4 MRR(g/min) 0.05354 0.32140 SR (µm) 1.429 1.433 GRG 0.6695 0.8603 0.9277 Improvement of the GRG = 0.2582 (38.57% of improvement observed) 4.4. Machined surface morphology Figure 7 a) shows the machined surface morphology obtained from experimental run 1 area, which illustrates that molten droplets are trapped on the surface in a globule shape. The presence of melt deposits, tiny globules, and deep craters is clearly noticeable on the machined surface. These cutting imperfections resulted in a poor surface finish. On the other hand, Fig. 7 b) shows the machined surface obtained with optimised machining parameters, which reveals that the existence of cracks, craters, and molten droplets in the form of globules has been considerably decreased, and melt deposits have been substantially eliminated resulting in a better surface finish. Figure 7 c) illustrates the EDS analysis and surface morphology of the wire electrode employed to machine SLMed AlSi10Mg specimen under optimum conditions. EDS analysis demonstrates that the zinc level in the wire electrode is higher than the copper content, and the SEM picture illustrates the surficial damage that occurred. The results reveal that surface properties (redeposited debris, cracks, craters, and micro-pores) are firmly associated with the discharge energy phenomena that occur throughout the process. It has been found that successive discharges have generated machined surfaces with craters, micropores and cracks. Furthermore, the occurrence of surface tension in the material melt pool is responsible for the formation of spherical modules [ 53 ]. Crack development is influenced not only by machining variables but also by different material properties, including thermal expansion coefficient, thermal conductivity, Young’s modulus, and tensile strength. Frequent heating and cooling by deionised water results in the generation of cracks. This heating and cooling enhance the yield stress, and the material is plastically deformed during heating, leading to tensile stresses on the surface and crack formation [ 54 ]. As mentioned earlier, the optimal parameter setting used for machined surfaces comprises dendritic regions with smooth surfaces and numerous fine surface microcracks. Compared with the surface morphology obtained at initial parameter settings to surface machined at optimum parameters, it revealed a corresponding reduction in surface defects and hence no additional rise in SR value even though MRR increased significantly. The reduction in surface defects at optimal process parameter levels is also related to the pulse On time of 118 µs, pulse Off time of 44 µs, servo voltage of 60 V, and wire feed rate of 7 m/s at which the spark intensity interfered with the machined surface is minimal, resulting in smoother surface. 5. Conclusions Wire Electrical Discharge Machining (Wire-EDM) of SLMed AlSi10Mg as-built part was thoroughly investigated to achieve multi-response optimisation employing Taguchi integrated Grey relational analysis. The observations are listed below: The impacts of machining variables such as pulse On time (A), pulse Off time (B), servo voltage (C), and wire feed rate (D) on the multi-response has been investigated with Grey Relational Analysis to increase the material removal rate as well as decrease surface roughness. As determined by grey relational grades, the optimal combination of Wire-EDM parameters expressed is A3B1C4D4 (pulse On time of 118 µs (Level 3), pulse Off time of 44 µs (Level 1), servo voltage of 60 V, and wire feed rate of 7 m/s (Level 4)). ANOVA revealed that the wire feed rate (58.31%) had an important influence on the machining performance of the SLMed AlSi10Mg as-built part compared to all other process parameters. Further, this optimisation increased multi-response characteristics by 38.57%. Compared to the machined surface under initial machining conditions, the part machined under optimised machining conditions has reduced defects in its surface morphology. Furthermore, it was also found that, while MRR grew significantly, SR did not rise significantly, and this is considered an advantageous outcome under ideal machining parameters. As a result, the Taguchi integrated Grey relational analysis is an efficient optimisation tool for optimising the wire-EDM process parameters of SLMed AlSi10Mg as-built parts with maximum material removal rate and significantly reduced surface roughness. Declarations Author Contributions All authors contributed to the study conception and design. Wire EDM experiments were performed by Murali Krishnan R, Saiyathibrahim A and Rajesh Ranganathan, Metallographic examinations were performed by Rajkumar Velu. Data analysis and collection were performed by Vijaykumar S Jatti and Dhanesh G. Mohan. The concept and the experimental plan were designed, and the manuscript was written by Murali Krishnan R and Saiyathibrahim A. All authors read and approved the final manuscript. Funding The authors declare that no funds, grants, or other support were received during the preparation of this manuscript. Competing Interests The authors have no relevant financial or non-financial interests to disclose. Ethical Approval The content studied in this article belongs to the field of metal processing and does not involve humans and animals. This article strictly follows the accepted principles of ethical and professional conduct. Consent to Participate All the authors consent to participate in this work. Consent to Publish All the authors in this work have consented to publish this manuscript. Availability of data and materials All necessary data are shown in the figures and tables within the document. 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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-4494311","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":310847197,"identity":"0180918e-bc4c-4d5a-b0c1-ee3f9fd6af0f","order_by":0,"name":"Murali Krishnan 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Technology","correspondingAuthor":false,"prefix":"","firstName":"Rajesh","middleName":"","lastName":"Ranganathan","suffix":""},{"id":310847199,"identity":"5095895e-5d34-42b1-b3fa-9d4c29ca16f7","order_by":2,"name":"Saiyathibrahim A","email":"","orcid":"","institution":"Karpagam Institute of Technology","correspondingAuthor":false,"prefix":"","firstName":"Saiyathibrahim","middleName":"","lastName":"A","suffix":""},{"id":310847200,"identity":"3b6138fd-eba1-4fa3-b19a-471b2107b18b","order_by":3,"name":"Rajkumar Velu","email":"","orcid":"","institution":"Indian Institute of Technology Jammu, Jammu and Kashmir","correspondingAuthor":false,"prefix":"","firstName":"Rajkumar","middleName":"","lastName":"Velu","suffix":""},{"id":310847201,"identity":"392f33b4-8137-407c-bc77-6ff05ae2f634","order_by":4,"name":"Vijaykumar S Jatti","email":"","orcid":"","institution":"Symbiosis Institute of Technology, Symbiosis International (Deemed University)","correspondingAuthor":false,"prefix":"","firstName":"Vijaykumar","middleName":"S","lastName":"Jatti","suffix":""},{"id":310847202,"identity":"ff5b442a-0073-42da-86b3-53d7955db566","order_by":5,"name":"Dhanesh G Mohan","email":"","orcid":"","institution":"University of Sunderland","correspondingAuthor":false,"prefix":"","firstName":"Dhanesh","middleName":"G","lastName":"Mohan","suffix":""}],"badges":[],"createdAt":"2024-05-29 04:59:13","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4494311/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4494311/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":58184330,"identity":"4e17ddd3-94f2-4f54-9d92-7084e7b2c2a1","added_by":"auto","created_at":"2024-06-12 07:00:47","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":1083067,"visible":true,"origin":"","legend":"\u003cp\u003ea) Wire-EDM machine used for machining, b) Machining process, and c) Machined as-built AlSi10Mg part\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-4494311/v1/00309328ec1cf03d74dd7ca2.png"},{"id":58186056,"identity":"a00d27d7-9a2d-4457-b1f0-5dd759a22004","added_by":"auto","created_at":"2024-06-12 07:16:47","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":860144,"visible":true,"origin":"","legend":"\u003cp\u003ea) Optical microscopic image of SLMed AlSi10Mg as-built part, and b) SEM image of as-cast AlSi10Mg part with EDX\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-4494311/v1/5cf6264ac9e51926453c4ce1.png"},{"id":58184328,"identity":"bb5a6edd-df0f-46d9-98e3-cff4bcf2820f","added_by":"auto","created_at":"2024-06-12 07:00:47","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":116283,"visible":true,"origin":"","legend":"\u003cp\u003eXRD peaks of SLMed AlSi10Mg as-built sample\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-4494311/v1/096ab8257edf0c9545c5c198.png"},{"id":58184334,"identity":"1ea22ffc-e3bb-41c5-b24b-2d2a37756492","added_by":"auto","created_at":"2024-06-12 07:00:47","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":15350,"visible":true,"origin":"","legend":"\u003cp\u003eMain Effects plot for Grey Relational Grades\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-4494311/v1/af7e8a428971a8eac159b39f.png"},{"id":58186057,"identity":"92038803-87cd-40d0-a418-e0872f3360f9","added_by":"auto","created_at":"2024-06-12 07:16:47","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":68556,"visible":true,"origin":"","legend":"\u003cp\u003eContribution of investigated Wire-EDM parameters to multi-response of SLMed AlSi10Mg\u003c/p\u003e","description":"","filename":"floatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-4494311/v1/acf6bd04baf7b14eb318b8ee.png"},{"id":58185323,"identity":"8046747a-d881-4e5f-951a-36f9f9b6cca0","added_by":"auto","created_at":"2024-06-12 07:08:47","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":17813,"visible":true,"origin":"","legend":"\u003cp\u003eProbability plot of Grey Relational Grade\u003c/p\u003e","description":"","filename":"floatimage6.png","url":"https://assets-eu.researchsquare.com/files/rs-4494311/v1/84bb1c494e119bbca973134b.png"},{"id":58185325,"identity":"b3ba4a0b-5442-4e50-9d79-1ac7cc0aae48","added_by":"auto","created_at":"2024-06-12 07:08:47","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":456686,"visible":true,"origin":"","legend":"\u003cp\u003ea) SEM images of surface machined with initial parameters, b) SEM images of surface machined with optimised parameters, and c) SEM and EDX images of Wire-EDM\u003c/p\u003e","description":"","filename":"floatimage7.png","url":"https://assets-eu.researchsquare.com/files/rs-4494311/v1/44f019162b1f2e3ffed0f6e8.png"},{"id":58544838,"identity":"c4df76fd-2acd-49e6-a831-0462c1979adb","added_by":"auto","created_at":"2024-06-18 05:26:28","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":4712325,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4494311/v1/45a55afa-cf5e-4c1f-9669-64d04d1bb025.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Multi-response Optimisation of Wire-EDM for SLMed AlSi10Mg using Taguchi-Grey Relational Theory","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eAdditive Manufacturing (AM) allows the production of components incorporating a high degree of complexity that traditional subtractive manufacturing cannot achieve [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Metal additive manufacturing has been gaining significant interest in both research and industrial sectors because of its ease of producing intricate components [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Selective laser melting (SLM) is one of the types of metal AM that is highly advantageous and belongs to the category of powder bed fusion (PBF). It can produce complicated metal components layer-by-layer using digital models with maximum flexibility and greater accuracy, reducing processing cycles and material waste [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Aluminium alloys (AlSi12, AlSi12Mg, AlSi10Mg), different types of steels (SS 316L, hot-work steel, tool steel, maraging steel), titanium alloys (Ti6Al4V, Ti6Al7Nb), Inconel 718, Inconel 625, cobalt alloys, gold, copper, etc. are among the popular materials processed by SLM [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. In particular, the demand for aluminium alloys has increased due to their greater utilisation in producing superior-quality commercial goods, which suggests that research into their functionality has become a top priority for researchers [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eAlSi10Mg is a nearly eutectic, castable alloy that finds high demand in the aviation and automobile sectors due to its low weight, minimal thermal expansion, and excellent mechanical characteristics [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Processing intricate AlSi10Mg parts through SLM seems beneficial because it results in sound as-built structures with good dimensional stability. The mechanical and surficial properties of as-built AlSi10Mg parts are connected to utilising optimised SLM process parameters and employing post-processing routes. Synthesising pore-free, dense, stable SLMed AlSi10Mg has been thoroughly investigated with several optimised operating parameters [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. In recent years, a significant amount of research has been carried out towards the surface quality of SLMed AlSi10Mg products. It is well known that good surface quality is always an expectation in any manufacturing process, and poor surface characteristics induce cracks in parts [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. A complex part is built in the SLM route using many support structures holding overhanging areas to obtain structural stability. These are to be removed through post-processing techniques without creating damage to the build part/component [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. However, cyclic layer-by-layer application of material and fusion process results in poor surface characteristics in components compared to parts made from conventional manufacturing routes [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. The most common issue in metal AM techniques is surface irregularities exhibiting poor morphology and randomly oriented features formed due to continual layer-by-layer deposition and fusion [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. A careful investigation of surface characteristics of as-built AM parts is most required since such surface features significantly influence effective performance. Hence, improving surface quality is necessary to oppose the establishment of mechanical failures of AM parts that are extremely dependent on surface properties [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eRecently, some surface post-treatments have been proposed by researchers aiming to impart good surface quality in metal AM parts [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. Boban \u0026amp; Ahmed [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e] proposed a novel EDIP (Electric Discharge Internal Processing) technique to post-process holes of AlSi10Mg blocks printed using LPBF (Laser Powder Bed Fusion) and suggested that this adopted technique enhances surface finish by removing internal surface irregularities. Atzeni et al. [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e] employed the Abrasive Fluidized Bed (AFB) route to enhance the surface quality of AlSi10Mg build part printed using Direct Metal Laser Sintering (DMLS), and the observed results showed that shorter operational cycles are recommended to produce good surfaces and there are again surface damages created due to impact of abrasive particles at the edges. Peng et al. [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e] investigated the surface finish and compressive stress behaviour of additively manufactured AlSi10Mg using Abrasive Flow Machining (AFM) and showed effectiveness in improving the surface integrity of the as-built part. Teng et al. [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e] combined the conventional grinding process and Magnetic Abrasive Finishing (MAF) to minimise surface roughness and enhance the surface quality of the SLMed AlSi10Mg part, and the results showed it to be beneficial. A similar response was identified when using disk laser-aided heat treatments combined with glass bead blasting during surface quality analysis SLMed AlSi10Mg [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. It was also identified during the chemical polishing of this same material in another investigation that surface finishes largely improved by around 80% [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. Schneller et al. [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e] assessed the effect of surface quality on the fatigue property of SLMed AlSi10Mg and found an increase in surface quality of around 60% when employing Hot Isostatic Pressing (HIP). From the above discussion, it can be understood that only limited research has been carried out so far in this field. In addition, limited techniques are only considered to investigate surface characteristics of the SLMed AlSi10Mg part. Vaidyaa et al. [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e] optimised Wire-EDM parameters for better surface characteristics of SLMed AlSi10Mg part employing Genetic Algorithm (GA) coupled Artificial Neural Network (ANN), through which 15% of improvement was attained. It is well known that Wire-EDM has been showing better machining performance since its inception, and precise quality machining can be conducted effectively using this process [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. Since AM parts are largely employed in aerospace and automotive sectors, higher surface finish is always mandatory, which can be achieved by efficiently implementing Wire-EDM to remove support structures and uneven surfaces created during layer-by-layer processing of AM. In addition, it is a non-contact advanced machining process, resulting in less thermal stress on the machined parts. Further, optimising process parameters results in better outcomes with shorter manufacturing time [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. Consequently, it is appropriate to implement this advantageous approach to increase the surface quality of SLM printed parts.\u003c/p\u003e \u003cp\u003ePrevious literature indicates significant potential for enhancing the surface properties of SLMed AlSi10Mg by utilising Wire-EDM and implementing multi-objective optimisation. Hence, this work aims to identify the most effective combination of machining parameters that may yield a greater material removal rate while minimising surface roughness. This will be accomplished using Taguchi integrated Grey Relational Analysis (GRA), a strategy that, to the best of our knowledge, has not been previously described.\u003c/p\u003e \u003cp\u003eIn this study, the AlSi10Mg part was built using SLM and its microstructural variations were observed. To obtain a better surface finish on the SLMed AlSi10Mg part, Wire-EDM parameters were optimised using Taguchi-grey relational theory. In addition to surface characteristics, the rate of material removal during Wire-EDM was also included for investigation since lesser processing time resulting in higher material removal is always beneficial. SEM morphology of machined surfaces was discussed elaborately to identify surface features.\u003c/p\u003e"},{"header":"2. Optimisation Procedure","content":"\u003cp\u003eMachining of additively fabricated AlSi10Mg specimen using Wire-EDM involves a number of performance characteristics, with Material Removal Rate (MRR) being a higher-the-better performance characteristic and Surface Roughness (SR) having the lower-the-better characteristic. Therefore, using Taguchi-based single response optimisation may improve one performance attribute at the expense of another. To resolve this issue, combining GRA with the Taguchi technique has been successfully reported to be beneficial in dealing with processes that incorporate several performance characteristics [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e].\u003c/p\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1. Taguchi method\u003c/h2\u003e \u003cp\u003eTaguchi's orthogonal array-based Design of Experiments (DOE) is an effective method for defining the ideal combination of process parameters with no repetitions. This method not only saves time and clearly specifies how to efficiently carry out the required number of trials, but it also provides complete information on all of the machining variables that affect the response variables economically. The core function of this orthogonal array method is to identify the influential process variables and their levels. Taguchi discovered three loss functions for qualitative analysis of real-time engineering applications that are highly effective for predicting the deviations of response parameters from their goal values. They are,\u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003eSmaller the better \u0026ndash; The best outcome is a lower value\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eLarger the better \u0026ndash; The best outcome is a higher value\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eNominal is the best \u0026ndash; The best result is mean value: between higher and lower values\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003cp\u003eThe Taguchi technique can generate an orthogonal array with fewer experiments using a set of input machining parameters and their pre-determined levels, avoiding experiment redundancy [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2. Grey Relational Analysis\u003c/h2\u003e \u003cp\u003eAs mentioned earlier, multi-response optimisation was carried out in this study utilising a Taguchi orthogonal array coupled with grey relational analysis (GRA). GRA is a decision-making technique based on Deng's grey system theory. The intention of GRA is to assess the degree of compactness and resemblance across sequences based on geometric shape. The grey relational grade quantifies the degree of link between response and reference sequences. The steps for performing GRA are as follows [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]:\u003c/p\u003e \u003cdiv id=\"Sec5\" class=\"Section3\"\u003e \u003ch2\u003e2.2.1. Normalising the results obtained from experiments\u003c/h2\u003e \u003cp\u003eThe response parameters MRR and SR were normalised linearly within a range of 0 to 1 using a technique referred to as grey relational generation. The outcomes of normalising can be represented as,\u003c/p\u003e \u003cp\u003eFor larger the better,\u003cdiv id=\"Equ1\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ1\" name=\"EquationSource\"\u003e\n$${X}_{h}\\left(P\\right)=\\frac{{X}_{h}^{^\\circ }\\left(p\\right) - min{X}_{j}^{^\\circ }\\left(p\\right) }{max{X}_{j}^{^\\circ }\\left(s\\right) - min{X}_{j}^{^\\circ }\\left(s\\right) }$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e1\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eFor smaller the better,\u003cdiv id=\"Equ2\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ2\" name=\"EquationSource\"\u003e\n$${X}_{h}\\left(P\\right)=\\frac{max{X}_{h}^{^\\circ }\\left(p\\right) - {X}_{h}^{^\\circ }\\left(p\\right) }{max{X}_{h}^{^\\circ }\\left(p\\right) - min{X}_{h}^{^\\circ }\\left(p\\right) }$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e2\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003ewhere, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({X}_{h}\\left(P\\right)\\)\u003c/span\u003e\u003c/span\u003e is the normalised value, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({\\text{X}}_{\\text{h}}^{\\text{\u0026deg;}}\\text{(p)}\\)\u003c/span\u003e\u003c/span\u003e is the original value, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\text{m}\\text{a}\\text{x}{\\text{ X}}_{\\text{h}}^{\\text{\u0026deg;}}\\text{(p) }\\)\u003c/span\u003e\u003c/span\u003eis the largest value among the original values and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\text{m}\\text{i}\\text{n}{\\text{ X}}_{\\text{h}}^{\\text{\u0026deg;}}\\text{(p)}\\)\u003c/span\u003e\u003c/span\u003e is the smallest value among the original values.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section3\"\u003e \u003ch2\u003e2.2.2. Calculating the Grey Relational Coefficients (GRC)\u003c/h2\u003e \u003cp\u003eAfter normalisation, the deviation sequence for each experiment must be computed. It is the difference between the reference and original sequences, which can be described as,\u003cdiv id=\"Equ3\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ3\" name=\"EquationSource\"\u003e\n$${\\varDelta }_{mn}\\left(P\\right)=\\left|{X}_{m}\\left(p\\right)-{X}_{h}\\left(p\\right)\\right|$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e3\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003ewhere, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({\\varDelta }_{mn}\\left(P\\right)\\)\u003c/span\u003e\u003c/span\u003eis the deviation sequence, and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({X}_{m}\\left(p\\right)\\)\u003c/span\u003e\u003c/span\u003eis the reference sequence.\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003eThese grey relational coefficients have been determined to illustrate the relationship between the experimental observations and ideal or better values. Grey Relational Coefficient can be presented as,\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Equ4\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ4\" name=\"EquationSource\"\u003e\n$${\\xi }_{h}\\left(P\\right)=\\frac{{\\varDelta }_{min}+\\xi {\\varDelta }_{max}}{{\\varDelta }_{mn}\\left(P\\right)+\\xi {\\varDelta }_{max}}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e4\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003ewhere \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\xi\\)\u003c/span\u003e\u003c/span\u003e is the identification coefficient varies from 0 to 1 and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({\\text{∆}}_{\\text{min}}\\)\u003c/span\u003e\u003c/span\u003e as well as \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({\\text{∆}}_{\\text{max}}\\)\u003c/span\u003e\u003c/span\u003e are the minimum and maximum deviations of each response parameter, respectively.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section3\"\u003e \u003ch2\u003e2.2.3. Calculating the Grey Relational Grades (GRG)\u003c/h2\u003e \u003cp\u003eIn order to illustrate the closeness of each experimental outcome produced to the ideal value, grey relational grades were computed. A higher GRG in an experimental result indicates that the finding is extremely significant. The grey relational grade is used to assess the overall quality of multiple responses. Consequently, optimising complicated multiple process outcomes may be reduced to a single grey relational grade. In simpler terms, GRG may be viewed as a thorough examination of data from experiments for the multi-response method. The grey relational grade (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({\\gamma }_{h}\\)\u003c/span\u003e\u003c/span\u003e) for any experiment was calculated using the following Eq.\u0026nbsp;(\u003cspan refid=\"Equ5\" class=\"InternalRef\"\u003e5\u003c/span\u003e),\u003cdiv id=\"Equ5\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ5\" name=\"EquationSource\"\u003e\n$${\\gamma }_{h}=\\frac{1}{n}\\sum _{h=1}^{n}{\\xi }_{h}\\left(P\\right)$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e5\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section3\"\u003e \u003ch2\u003e2.2.4. Analysis of Variance (ANOVA)\u003c/h2\u003e \u003cp\u003eANOVA is a statistical tool used to determine the effects of all input machining variables on the results. The primary objective of ANOVA is to use the grey relational grades obtained to classify the most significant machining parameter with its proportion of contribution for a specific response [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section3\"\u003e \u003ch2\u003e2.2.5. Confirmation test\u003c/h2\u003e \u003cp\u003eFinally, a confirmation test has been carried out to assess the dependability of multi-response optimisation utilising the optimal input machining parameters determined by grey relational grading. In order to produce the desired response, optimum machining parameters had been selected throughout this test, and the level of confidence was considered to be 90 to 95%.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"3. Materials and Methods","content":"\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e3.1. Fabrication of SLMed AlSi10Mg\u003c/h2\u003e \u003cp\u003eAlSi10Mg specimen with an approximate size of 50 x 50 x 10 mm was developed using the Selective Laser Melting (SLM) technique on an EOS M290 machine. The machine is provided with a building platform with dimensions of 250 \u0026times; 250 \u0026times; 325 mm\u003csup\u003e3\u003c/sup\u003e. The printing chamber operates in an inert atmosphere (Argon), with a laser power of 400 W and a focus diameter of 100 \u0026micro;m. The powder was continually supplied by the system with a dispenser unit supplying AlSi10Mg with 40 \u0026micro;m throughout the printing process. Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e shows the powder composition of AlSi10Mg and selected SLM process variables, which ensured the fabrication of fully dense components. The sample was manufactured horizontally (parallel to the powder bed X-Y plane) using selected parameters listed in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\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\u003eAlSi10Mg chemical composition\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"12\"\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 \u003cdiv align=\"left\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eElement\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSi\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCu\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eFe\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNi\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eZn\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eMg\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eMn\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003ePb\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003eSn\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c11\"\u003e \u003cp\u003eTi\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c12\"\u003e \u003cp\u003eAl\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ewt%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003eBalance\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\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\u003eSLM parameters employed for printing\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\"\u003e \u003cp\u003eSpecification/Process parameter\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUnit\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eType/Value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLaser beam spot size\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026micro;m\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e100\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAtmosphere\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eArgon\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLaser power\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eW\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e370\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLayer scan speed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003emm/s\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1300\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLayer thickness\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026micro;m\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHatch spacing\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003emm\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.19\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBuild platform temperature\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003csup\u003eo\u003c/sup\u003eC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e35\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVolume rate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003emm\u003csup\u003e3\u003c/sup\u003e/s\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e3.2. Wire-EDM of SLMed AlSi10Mg\u003c/h2\u003e \u003cp\u003eWire-EDM is a precision manufacturing process that uses a thin wire as a tool electrode to establish an electro-thermal material removal mechanism. In this approach, the machining zone is flooded with dielectric fluid. This fluid serves as an insulator, and the material is removed from the workpiece by a series of electrical discharges. In detail, the advancement of the wire towards the workpiece creates a gap in which a substantial voltage is created, which breaks the dielectric fluid, causing an electrical discharge and, therefore spark erosion material removal. For a precise cut, the procedure outlined above is repeated approximately 2,40,000 times per second. In addition to cooling, dielectric fluid flushes all machining byproducts from the machining gap [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. Figure\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e depicts the process of machining the SLMed AlSi10Mg part using the Wire-EDM process. The experimental runs of the SLMed AlSi10Mg part were performed on an Electronica Sprintcut Win Plus CNC Wire-EDM (five-axis) machine, and its detailed features are listed in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e. A 0.25 mm diameter brass (copper 60% + zinc 40%) wire electrode submerged in dielectric fluid was employed in this Wire-EDM process.\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\u003eSpecifications of Wire-EDM machine used\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"2\"\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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDescription\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSpecification\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eElectronica - Sprintcut Win Plus\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMaterial removal mechanism\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMelting and evaporation\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWorktable size\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e440 x 650 mm\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMain axes traverse (X, Y)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e300 x 400 mm\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAux. axes traverse (u, v)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e80 x 80 mm\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMax. workpiece height\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e250 mm\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMax. taper angle\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026plusmn;\u0026thinsp;30˚/50 mm\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eResolution\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.0005 mm\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMax. JOG speed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e900 mm/min\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMax. wire spool capacity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6 kg (up to DIN 160 / P5)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDielectric fluid\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDeionised water\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMax. workpiece weight\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e300 kg\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWire electrode\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBrass wire of 0.25 mm diameter\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 investigation was performed employing an L16 orthogonal array. Four key factors-pulse On time (A), pulse Off time (B), servo voltage (C), and wire feed rate (D)-were considered in order to transform the design into a four-level, four-factorial Taguchi design. Minitab 19 version software was used to develop this experimental design. The details of Wire-EDM parameters and their levels involved in framing the L\u003csub\u003e16\u003c/sub\u003e orthogonal array are depicted in Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e. Figure\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e shows the Wire-EDM machine used for machining, machining process, and machined as-built AlSi10Mg part. Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e shows the experimental runs generated by integrating machining parameters of various levels and the responses observed.\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\u003eWire-EDM parameters 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=\"char\" char=\".\" 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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eParameters\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eUnits\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"4\" nameend=\"c6\" namest=\"c3\"\u003e \u003cp\u003eLevels\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLevel 1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLevel 2\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eLevel 3\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eLevel 4\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePulse On Time (A)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026micro;s\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e114\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e116\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e118\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e120\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePulse Off Time (B)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026micro;s\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e47\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eServo Voltage (C)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eV\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e60\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWire Feed Rate (D)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003em/s\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e7\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 \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\u003eL16 orthogonal array with observed responses\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=\"char\" char=\".\" 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\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eExp. Run\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"4\" nameend=\"c5\" namest=\"c2\"\u003e \u003cp\u003eWire-EDM parameters\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003eResponse parameters\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePulse On Time\u003c/p\u003e \u003cp\u003e(A)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePulse Off Time\u003c/p\u003e \u003cp\u003e(B)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eServo Voltage\u003c/p\u003e \u003cp\u003e(C)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eWire Feed Rate\u003c/p\u003e \u003cp\u003e(D)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eMaterial Removal Rate\u003c/p\u003e \u003cp\u003e(MRR)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eSurface Roughness\u003c/p\u003e \u003cp\u003e(SR)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(\u0026micro;s)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(\u0026micro;s)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(V)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(m/s)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(g/min)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(\u0026micro;m)\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\u003e114\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.05354\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.429\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\u003e114\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.20650\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e2.613\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\u003e114\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.09501\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e3.141\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\u003e114\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.32505\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e2.640\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\u003e116\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.15296\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e3.135\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\u003e116\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.34608\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e2.041\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\u003e116\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.30593\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e2.219\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\u003e116\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.24665\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e3.378\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\u003e118\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.32505\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.755\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e118\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.14723\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e2.224\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e118\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.16004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e2.019\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e118\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.04589\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.697\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e120\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.10325\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.820\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e120\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.22885\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e3.548\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e120\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.26004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e3.463\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e120\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.13002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e3.629\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e3.3. Microstructural observation\u003c/h2\u003e \u003cdiv id=\"Sec14\" class=\"Section3\"\u003e \u003ch2\u003e3.3.1. Surface morphology\u003c/h2\u003e \u003cp\u003eSurface morphology plays an essential role in assessing the quality of machined surfaces. Furthermore, printed specimens in their as-built form, as well as as-cast specimens, are evaluated by utilising an optical microscope (Carl Zeiss Axioscope 5) and Scanning Electron Microscope (SEM) with Energy Dispersive X-ray spectroscopy (EDX) (JSM-IT200 InTouchScope). All microstructural samples were prepared by hand polishing with various grades of emery sheets, followed by disc polishing, and finally, an etching process using Keller's reagent of 95 ml distilled water, 2.5 ml HNO\u003csub\u003e3\u003c/sub\u003e, 1.5 ml HCl, and 1 ml HF for 30 seconds to disclose the microstructure. In addition,\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section3\"\u003e \u003ch2\u003e3.3.2. X-ray diffraction (XRD)\u003c/h2\u003e \u003cp\u003eTo assess the phase composition, X-ray diffraction was carried out on an as-built SLMed AlSi10Mg sample using a continuous scan mode of 10\u0026deg;/min utilising an Empyrean Series III Diffractometer having phase composition shifted from 20\u0026deg; to 90\u0026deg; and employing X-ray source of Cu Kα (λ\u0026thinsp;=\u0026thinsp;1.54 \u0026Aring;) anticathode at 35 kV and 40 mA.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003e3.4. Measurement of Surface Roughness (SR)\u003c/h2\u003e \u003cp\u003eSurface roughness can be expressed as a variation of the actual surface measured along the normal vector direction from the ideal surface value. A rough surface generates a lot of deviation, whereas a smooth one produces very minimal [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. The surface roughness of a machined specimen is thought to have a significant influence on its mechanical properties. This inquiry examines it by utilising a Mitutoyo SJ-210 surface roughness tester. Surface roughness values were measured three times at each machined region, and the average of these observations was adopted as the final value [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003e3.5. Measurement of Material Removal Rate (MRR)\u003c/h2\u003e \u003cp\u003eMaterial Removal Rate constitutes one of the most essential response variables to consider while machining a component/part. It has a substantial relationship with product quality, production rate, and manufacturing efficiency. MRR is the rate at which material is removed on a manufactured part in a specific duration [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. MRR has been calculated for each experimental run using the weight loss technique during machining, which is displayed in Eq.\u0026nbsp;(\u003cspan refid=\"Equ6\" class=\"InternalRef\"\u003e6\u003c/span\u003e). After finishing each experimental run, a digital weighing balance (accuracy\u0026thinsp;=\u0026thinsp;0.001 g) was utilised to quantify the before and after weight of the machining specimen. A digital weighing balance (accuracy\u0026thinsp;=\u0026thinsp;0.001 g) was used to measure the initial\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({ (W}_{i})\\)\u003c/span\u003e\u003c/span\u003e and final\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({ (W}_{f})\\)\u003c/span\u003e\u003c/span\u003e weight of machining specimen after completing each experimental run [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e].\u003cdiv id=\"Equ6\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ6\" name=\"EquationSource\"\u003e\n$$MRR=\\frac{{W}_{i}\\times {W}_{f}}{t} (\\text{g}/\\text{m}\\text{i}\\text{n})$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e6\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003c/div\u003e"},{"header":"4. Results and Discussions","content":"\u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003e4.1. Microstructural observation of SLMed AlSi10Mg as-built specimen\u003c/h2\u003e \u003cp\u003eThe optical microstructure of the SLMed AlSi10Mg printed part is shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ea). The interfacial microstructure features created by pulsating laser beam motion consist of stretched teardrops from molten pool microstructure as well as melting boundary layers generated through continual wave laser beam movement. There is an evident multilayered structure and elliptical molten pools overlaying each other along the line of laser movement [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. Specifically, the microstructure is composed of superimposed melt pools produced by sequential laser rastering, leading to the melting and solidifying of consecutive powder layers. An exceptional fine dendritic structure of the α-Al matrix governed by the eutectic Si phase arrived within the melt pools. The rapid cooling rates employed in the SLM technique resulted in the formation of an extremely fine structure. The growth of a hierarchical microstructure in SLMed AlSi10Mg is an essential characteristic resulting from distinct solidification conditions during the SLM process [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eFor comparison, an as-cast AlSi10Mg sample was additionally synthesised by gravity casting, and its microstructure was examined using SEM/EDX (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eb)), and it differs significantly from the part printed by SLM. The processing technique clearly has a significant influence on microstructures and as-cast samples consisting of eutectic silicon and primary Mg\u003csub\u003e2\u003c/sub\u003eSi intermetallic phase in aluminium matrix, as proven by EDX observation (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eb)). It should also be mentioned that the solid solution of silicon in aluminium dissolves and precipitates in a considerably coarser form due to the slower cooling rate employed in a typical AlSi10Mg gravity casting procedure [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. As mentioned earlier, typical α-Al phase and eutectic Si were observed in the SLMed AlSi10Mg sample. The rapid cooling nature of the SLM process enables Si to become supersaturated and precipitate in the Al matrix [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. Owing to the continual exposure of the printing part to heating cycles during the SLM process, Mg and Si atoms diffused into the supersaturated α-Al matrix and formed Mg\u003csub\u003e2\u003c/sub\u003eSi precipitates. These precipitates are believed to enhance strength by minimising dislocation motion in the printed part [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. Furthermore, both microstructures reveal no porosity problems, indicating that excellent experimentation was carried out.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe XRD spectra of the SLMed as-built AlSi10Mg sample are shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, and it demonstrates a very low intensity of Si peaks due to less silicon (9.8 wt%) in the examined material compared to aluminium, suggesting Mg\u003csub\u003e2\u003c/sub\u003eSi establishment [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e].\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003e4.2. Multi-response Optimization using Grey relational Analysis (GRA)\u003c/h2\u003e \u003cp\u003eThe Wire-EDM behaviour of SLMed AlSi10Mg was satisfactorily examined, and Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e shows the Grey Relational Grades (GRGs) generated by pre-processed responses. Because of the heterogeneity in measurement units, all recorded original responses were normalised using Equations \u003cspan refid=\"Equ1\" class=\"InternalRef\"\u003e1\u003c/span\u003e and \u003cspan refid=\"Equ2\" class=\"InternalRef\"\u003e2\u003c/span\u003e during pre-processing and transformed into values ranging from 0 to 1. The normalised response values and their associated deviation sequences are shown in Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e. The deviation sequences were generated using Eq.\u0026nbsp;\u003cspan refid=\"Equ3\" class=\"InternalRef\"\u003e3\u003c/span\u003e and then utilised to determine the relevant grey relational coefficients and grey relational grades using Equations \u003cspan refid=\"Equ4\" class=\"InternalRef\"\u003e4\u003c/span\u003e and \u003cspan refid=\"Equ5\" class=\"InternalRef\"\u003e5\u003c/span\u003e. The identification coefficient (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\xi\\)\u003c/span\u003e\u003c/span\u003e) was set to 0.5 as per the standard approach to ensure equal weightage throughout the calculation of grey relational coefficients for each response. Abegunde et al. [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e] suggested choosing a higher grey relational grade to obtain more effective multi-response characteristics. Furthermore, the ideal combination of machining parameters is often indicated by the higher grey relational grade assigned to rank 1 [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]. The ranking of each experiment and the values of grey relational grades are displayed in Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e.\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\u003eGrey Relational Grades obtained from pre-processing of responses\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"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\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eExp. Run\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003ePre-processed responses (Ideal\u0026thinsp;=\u0026thinsp;1)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003eGrey Relational Coefficient (GRC)\u003c/p\u003e \u003cp\u003e(Ideal\u0026thinsp;=\u0026thinsp;1)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eGrey Relational Grade\u003c/p\u003e \u003cp\u003e(GRG)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eOrder\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMRR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMRR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSR\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\u003e0.025478\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.339093\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.6695\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e5\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\u003e0.535032\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.461818\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.518152\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.481611\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.4999\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e10\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\u003e0.163631\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.221818\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.374148\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.391181\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.3827\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e15\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\u003e0.929936\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.449545\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.877095\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.475984\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.6765\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e4\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=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.356688\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.224545\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.437326\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.392017\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.4147\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e14\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=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.721818\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.642523\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.8213\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e2\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=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.866242\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.640909\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.788945\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.582011\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.6855\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e3\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=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.66879\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.114091\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.601533\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.360774\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.4812\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e12\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=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.929936\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.851818\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.877095\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.771388\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0.8242\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e1\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.33758\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.638636\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.430137\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.580475\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.5053\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.380255\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.731818\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.44653\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.650888\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.5487\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.878182\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.333333\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.804094\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.5687\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.191083\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.822273\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.381995\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.73776\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.5599\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.60949\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.036818\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.561476\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.341721\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.4516\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.713376\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.075455\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.635628\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.350989\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.4933\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.280255\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.409922\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.333333\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.3716\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e16\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\u003eAnalysis of Variance (ANOVA) has been carried out effectively in this investigation using Minitab 19 statistical software. Table\u0026nbsp;\u003cspan refid=\"Tab7\" class=\"InternalRef\"\u003e7\u003c/span\u003e displays the impact of each process parameter on the basis of GRG at each level. It also displays the computed total mean of grey relational grades for all 16 experimental runs, which comes out to be 0.5597. A grey relational grade represents the degree of correlation between the reference sequence and the original sequence [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]. The original and reference sequences are more strongly correlated, as indicated by the higher value of the grey relational grade.\u003c/p\u003e \u003cp\u003eConsequently, the level with the highest grey relational grade value is the ideal level of machining parameters. An improved Wire-EDM performance is achieved by the parameters indicated by the level value that is shown with an asterisk (*). The most effective machining performance for SR and MRR was identified for the combination of pulse On time (A) (level 3), pulse Off time (B) (level 1), servo voltage (C) (level 3), and wire feed rate (D) (level 4), based on the higher grey relational grade presented in Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e.\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\u003eGrey Relational Grades obtained from pre-processing of responses\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=\"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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eWire EDM parameters\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"4\" nameend=\"c5\" namest=\"c2\"\u003e \u003cp\u003eLevels\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eMax - Min\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLevel 1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLevel 2\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLevel 3\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eLevel 4\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePulse On Time (A)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.5572\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.6006\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.6117*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.4691\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.1426\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePulse Off Time (B)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.6171*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.5695\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.5275\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.5245\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.0926\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eServo Voltage (C)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.6028\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.4941\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.5349\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.6068*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.1127\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWire Feed Rate (D)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.5938\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.5224\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.4186\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.7038*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.2853\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"6\" nameend=\"c6\" namest=\"c1\"\u003e \u003cp\u003eMean value of Grey Relational Grade\u0026thinsp;=\u0026thinsp;0.5597\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\u003eHowever, according to Table\u0026nbsp;\u003cspan refid=\"Tab7\" class=\"InternalRef\"\u003e7\u003c/span\u003e, in the context with several performance characteristics, A3B1C4D4 is the ideal level of Wire-EDM parameters since a higher mean value for each factor provides better outcomes. Additionally, the variation between maximum and minimum values of grey relational grades has been estimated and tabulated in Table\u0026nbsp;\u003cspan refid=\"Tab7\" class=\"InternalRef\"\u003e7\u003c/span\u003e. For the Wire-EDM parameters, the deviation between the maximum and minimal values of the average grey relational grade is as follows: 0.1426 for the pulse On time, 0.0926 for the pulse Off time, 0.1127 for the servo voltage, and 0.2853 for the wire feed rate. By comparing these data, the most essential parameter influencing machining performance will be identified. The greatest of these numbers was the most effective controllable factor. Considering this, the wire feed rate has been identified as the machining parameter with the most influence, as it had the highest GRG mean value of 0.2853 compared to all other parameters investigated in the current study.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eIn addition, it is possible to estimate the significance of each machining parameter, aiming at multi-response characteristics of the printed specimen, by looking at the mean values of GRGs. Wire-EDM mean data values show the following influencing order: wire feed rate (D), pulse On time (A), servo voltage (C), and pulse Off time (B). As stated earlier, the wire feed rate has the most significant impact on the wire electrical discharge machining process for SLMed AlSi10Mg. Furthermore, as illustrated by the dashed line in the main effect plot graph (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e) constructed using estimated grey relational grades, the overall mean value obtained from this experimental investigation is 0.5597. In GRA, it is universally acknowledged that higher GRG mean values always represent more effective multi-response characteristics. The largest mean values, according to Table\u0026nbsp;\u003cspan refid=\"Tab7\" class=\"InternalRef\"\u003e7\u003c/span\u003e, are A (Level 3), B (Level 1), C (Level 4), and D (Level 4). In other words, the ideal machining combinations determined by the GRG indicate that the response table is A3B1C4D4. The maximum values in each curve designated to each parameter are shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e. These values are expected to improve Wire-EDM performance by producing low SR and high MRR.\u003c/p\u003e \u003cp\u003eANOVA is used to assess the influence of each parameter employed during Wire-EDM. The impact of each process parameter on the multi-response characteristics is effectively estimated through this statistical method [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e]. Table\u0026nbsp;\u003cspan refid=\"Tab8\" class=\"InternalRef\"\u003e8\u003c/span\u003e reveals the ANOVA results for the estimated GRGs of the Wire-EDM process. The percentage of contributions was determined for each machining parameter while accounting for error by processing the sum of squares values (variability between estimated GRG means). The percentage contribution calculated depending on the sum of squares values of process parameters during the Wire-EDM process of SLMed AlSi10Mg is shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e. Furthermore, the parametric test (F test) and probability values (P value) were also performed [\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e].\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\u003eResults of ANOVA on grey grade\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDrilling parameters\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDF\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSS\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMS\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eF-Value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eP-Value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eContribution\u003c/p\u003e \u003cp\u003e(%)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePulse On Time (A)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.05040\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.016798\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.169\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e16.99\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePulse Off Time (B)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.02265\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.007549\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.365\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e7.63\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eServo Voltage (C)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.03595\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.011982\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.241\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e12.12\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWire Feed Rate (D)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.17300\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.057667\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e11.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.036\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e58.31\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eError\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.01469\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.004897\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e4.95\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.29668\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe ANOVA results indicate that, when P values were computed, one of the four machining parameters under investigation had values less than 0.05. Due to this, the machining performance is greatly influenced by that one parameter (95% confidence level). The wire feed rate (D) has a more significant impact on the multi-response than the pulse On time (A), pulse Off time (B), and servo voltage (C), despite the fact that it is significant at the 95% confidence level. In summary, several parameters influenced the Wire-EDM of SLMed AlSi10Mg, and the details of each factor, along with their percentage contribution, are shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eRamanujam et al. [\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e] identified that wire feed is the most influential parameter in collectively affecting surface roughness and material removal rate. According to Samal et al. [\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e], the wire feed rate, pulse On time, and servo voltage together substantially impact the MRR. These previous findings support the findings of this study, which show in Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e that the key essential parameters, including wire feed rate, should be at maximum (7 m/s), and it is ideal to have a pulse On time of 118 \u0026micro;s and servo voltage would be 60 V in order to attain better machining characteristics. Hence, it is clear that MRR was substantially improved by the increase in wire feed rate as well as higher pulse. On duration causes a rapid surge in thermal energy, which adds to higher workpiece erosion [\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e]. It also suggested from the understanding of the study that a higher wire feed rate generates smoother surfaces than an inferior wire feed rate. Because many craters may emerge in the very same area on the wire surface as long as the wire feed has been excessively low, which may result in wire breakages [\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e]. A higher pulse On time leads to a larger discharge energy, which elevates the amount of material removed. As a result, increasing the pulse duration, i.e., power and energy, elevates MRR [\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e]. The current study concludes that the impact of servo voltage and pulse Off time on producing lower surface roughness and a greater material removal rate is comparatively lower. Gupta et al. [\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e] revealed that the machining parameters such as servo voltage and wire feed substantially influence the machinability of the Wire-EDM process, and optimising these variables can result in better outcomes.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe Normal probability plot based on the Grey Relational Grade is depicted in Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e. With the exception of one deviation, all points are adjacent to a straight line, illustrating that the computed grey grade exhibits a normal distribution. Wherever there is a steep slope in the plot, it can be concluded that those parameters have a considerable impact on the Wire-EDM process [\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec21\" class=\"Section2\"\u003e \u003ch2\u003e4.3. Confirmation test\u003c/h2\u003e \u003cp\u003eBy examining the GRGs of multi-response, optimal Wire-EDM parameters were identified. A confirmation test validated the reliability of this analysis. Following the selection of optimal machining settings, the next step is to compute response improvement by performance analysis. This important step validates the findings of the analytical phase [\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e]. Therefore, a confirmation experiment was performed to confirm this prediction. Wire electrical discharge machining was performed precisely with a pulse On time of 118 \u0026micro;s (Level 3), a pulse Off time of 44 \u0026micro;s (Level 1), a servo voltage of 60 V (Level 4) and a wire feed rate of 7 m/s (Level 4). Table\u0026nbsp;\u003cspan refid=\"Tab9\" class=\"InternalRef\"\u003e9\u003c/span\u003e shows the confirmation test results using the optimal combination of Wire-EDM parameters. To better understand this multi-response optimisation study, the grey relational grade of the confirmatory experiment is compared with the reference experiment (Exp. Run 1 in Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). It shows that when the material removal rate increases from 0.05354 to 0.32140 g/min, the surface roughness is highly steady and increases slightly from 1.429 to 1.433 \u0026micro;m, which is comparably minor. For this multi-response research, the grey relational grade improved by 0.2582 units. As shown in Table\u0026nbsp;\u003cspan refid=\"Tab9\" class=\"InternalRef\"\u003e9\u003c/span\u003e, there is a considerable improvement in GRG of 38.57%. The Taguchi technique, in connection with the GRA multi-response optimisation results, is satisfying and offers new insights into the machining capabilities of AlSi10Mg.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab9\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 9\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eConfirmation test results\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eInitial\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003eOptimal Wire EDM parameters\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePredicted\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eExperimental\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCombination\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eA1B1C1D1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eA3B1C4D4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eA3B1C4D4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMRR(g/min)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.05354\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.32140\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSR (\u0026micro;m)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.429\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.433\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGRG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.6695\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.8603\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.9277\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e \u003cp\u003eImprovement of the GRG\u0026thinsp;=\u0026thinsp;0.2582 (38.57% of improvement observed)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec22\" class=\"Section2\"\u003e \u003ch2\u003e4.4. Machined surface morphology\u003c/h2\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003ea) shows the machined surface morphology obtained from experimental run 1 area, which illustrates that molten droplets are trapped on the surface in a globule shape. The presence of melt deposits, tiny globules, and deep craters is clearly noticeable on the machined surface. These cutting imperfections resulted in a poor surface finish. On the other hand, Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eb) shows the machined surface obtained with optimised machining parameters, which reveals that the existence of cracks, craters, and molten droplets in the form of globules has been considerably decreased, and melt deposits have been substantially eliminated resulting in a better surface finish. Figure\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003ec) illustrates the EDS analysis and surface morphology of the wire electrode employed to machine SLMed AlSi10Mg specimen under optimum conditions. EDS analysis demonstrates that the zinc level in the wire electrode is higher than the copper content, and the SEM picture illustrates the surficial damage that occurred.\u003c/p\u003e \u003cp\u003eThe results reveal that surface properties (redeposited debris, cracks, craters, and micro-pores) are firmly associated with the discharge energy phenomena that occur throughout the process. It has been found that successive discharges have generated machined surfaces with craters, micropores and cracks. Furthermore, the occurrence of surface tension in the material melt pool is responsible for the formation of spherical modules [\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e]. Crack development is influenced not only by machining variables but also by different material properties, including thermal expansion coefficient, thermal conductivity, Young\u0026rsquo;s modulus, and tensile strength. Frequent heating and cooling by deionised water results in the generation of cracks. This heating and cooling enhance the yield stress, and the material is plastically deformed during heating, leading to tensile stresses on the surface and crack formation [\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eAs mentioned earlier, the optimal parameter setting used for machined surfaces comprises dendritic regions with smooth surfaces and numerous fine surface microcracks. Compared with the surface morphology obtained at initial parameter settings to surface machined at optimum parameters, it revealed a corresponding reduction in surface defects and hence no additional rise in SR value even though MRR increased significantly. The reduction in surface defects at optimal process parameter levels is also related to the pulse On time of 118 \u0026micro;s, pulse Off time of 44 \u0026micro;s, servo voltage of 60 V, and wire feed rate of 7 m/s at which the spark intensity interfered with the machined surface is minimal, resulting in smoother surface.\u003c/p\u003e \u003c/div\u003e"},{"header":"5. Conclusions","content":"\u003cp\u003eWire Electrical Discharge Machining (Wire-EDM) of SLMed AlSi10Mg as-built part was thoroughly investigated to achieve multi-response optimisation employing Taguchi integrated Grey relational analysis. The observations are listed below:\u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003eThe impacts of machining variables such as pulse On time (A), pulse Off time (B), servo voltage (C), and wire feed rate (D) on the multi-response has been investigated with Grey Relational Analysis to increase the material removal rate as well as decrease surface roughness.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eAs determined by grey relational grades, the optimal combination of Wire-EDM parameters expressed is A3B1C4D4 (pulse On time of 118 \u0026micro;s (Level 3), pulse Off time of 44 \u0026micro;s (Level 1), servo voltage of 60 V, and wire feed rate of 7 m/s (Level 4)).\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eANOVA revealed that the wire feed rate (58.31%) had an important influence on the machining performance of the SLMed AlSi10Mg as-built part compared to all other process parameters. Further, this optimisation increased multi-response characteristics by 38.57%.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eCompared to the machined surface under initial machining conditions, the part machined under optimised machining conditions has reduced defects in its surface morphology. Furthermore, it was also found that, while MRR grew significantly, SR did not rise significantly, and this is considered an advantageous outcome under ideal machining parameters.\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003cp\u003eAs a result, the Taguchi integrated Grey relational analysis is an efficient optimisation tool for optimising the wire-EDM process parameters of SLMed AlSi10Mg as-built parts with maximum material removal rate and significantly reduced surface roughness.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAuthor Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll authors contributed to the study conception and design. Wire EDM experiments were performed by Murali Krishnan R, Saiyathibrahim A and Rajesh Ranganathan, Metallographic examinations were performed by Rajkumar Velu. Data analysis and collection were performed by Vijaykumar S Jatti and Dhanesh G. Mohan. The concept and the experimental plan were designed, and the manuscript was written by Murali Krishnan R and Saiyathibrahim A. All authors read and approved the final manuscript.\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that no funds, grants, or other support were received during the preparation of this manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting Interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors have no relevant financial or non-financial interests to disclose.\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthical Approval\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe content studied in this article belongs to the field of metal processing and does not involve humans and animals. This article strictly follows the accepted principles of ethical and professional conduct.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to Participate\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAll the authors consent to participate in this work.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to Publish\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll the authors in this work have consented to publish this manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll necessary data are shown in the figures and tables within the document. The raw data can be made available upon request.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003e\u003cspan\u003eSaravana Kumar M, Mohan E, Robinson S, Thivya Prasad D (2022) Comparative study on morphological, physical and mechanical characteristics of L-PBF based AlSi10Mg parts with conventional stir casted Al-10% SiC composites. Silicon, pp 1\u0026ndash;12. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s12633-021-01065-9\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\n \u003cli\u003e\u003cspan\u003eGorsse S, Hutchinson C, Goun\u0026eacute; M, Banerjee R (2017) Additive manufacturing of metals: a brief review of the characteristic microstructures and properties of steels, Ti-6Al-4V and high-entropy alloys. 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Measurement 131:694\u0026ndash;700. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.measurement.2018.09.038\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\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":"SLM, AlSi10Mg, Wire-EDM, Taguchi orthogonal array, Grey relational analysis (GRA), SEM","lastPublishedDoi":"10.21203/rs.3.rs-4494311/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4494311/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe present research effort strives to optimise the multi-response during Wire Electrical Discharge Machining (Wire-EDM) of SLMed AlSi10Mg, applying Taguchi integrated Grey Relational Analysis (GRA). Selective Laser Melting (SLM) represents one of the best-known and most practicable Additive Manufacturing (AM) methods that have the prospective to serve as a replacement for many traditional production processes. Extremely intricate metallic support structures built up during SLM need more attention since they are too difficult to remove by hand. Therefore, post-processing adopting the Wire-EDM precision machining technique is performed in this study to assess the machinability of the SLMed AlSi10Mg as-built part. The multi-response optimisation used here seeks to achieve maximum material removal rate and lowest surface roughness while considering four important influencing elements (pulse On time, pulse Off time, servo voltage, and wire feed rate) at four distinct levels. Taguchi integrated Grey Relational Analysis (GRA) revealed that a pulse On time of 118 \u0026micro;s (Level 3), a pulse Off time of 44 \u0026micro;s (Level 1), a servo voltage of 60 V (Level 4), and a wire feed rate of 7 m/s (Level 4) are suggested to achieve optimal machining of SLMed AlSi10Mg. Furthermore, the derived optimisation results were diligently verified using a confirmatory experiment, and a 38.57% improvement in multi-response characteristics was found when compared to the initial Wire-EDM parameter settings. The methodology suggested in this work offers a standard approach that has the potential to be implemented for the rapid and precise prediction and optimisation of surface roughness while achieving better material removal during Wire-EDM of SLMed AlSi10Mg.\u003c/p\u003e","manuscriptTitle":"Multi-response Optimisation of Wire-EDM for SLMed AlSi10Mg using Taguchi-Grey Relational Theory","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-06-12 07:00:42","doi":"10.21203/rs.3.rs-4494311/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":"cb5e2789-bf15-440c-bcad-4631dc2a3ff2","owner":[],"postedDate":"June 12th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-06-18T05:18:18+00:00","versionOfRecord":[],"versionCreatedAt":"2024-06-12 07:00:42","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4494311","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4494311","identity":"rs-4494311","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}