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Use of machine learning and artificial intelligence (AI) to analyze the complex interdependencies of production dataset has gained momentum in recent years. Machine learning and predictive algorithms are now used by manufacturers to fine-tune the quality of their products. WEDM of SS304 with process parameters such as pulse-on-time (Ton), pulse-off-time (T off), current (I), and voltage (V) was varied to study the effect of machining parameters such as Material Removal Rate (MRR) and surface roughness. Experiments were planned and executed according to the L’9 orthogonal array. Scanning Electron Microscope (SEM) was utilized to study the machined surface. An analysis of variance (ANOVA) was performed to determine the input and output significance. ANOVA results revealed that V (81.85%) and Toff (77.75 %) for surface roughness. Further to determine the relationship between variables, various regression models based on machine learning was tested. The effectiveness of the regression models were tested. From their output it was concluded that the multilayer perception model had the highest correlation coefficient (0.999) for MRR while for surface roughness it was (0.995).

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A widely used material in engineering is aluminium reinforced with fillers called aluminium matrix composites (AMC). AA6061-T6 was selected as a matrix material in the present study, and silicon carbide (SiC) was chosen as the reinforcement, and the stir casting process produced the same. In the current study, the machining of AA6061-T6/15wt.% SiC composites were carried out using wire electrical discharge machining (WEDM). The input process parameter selected were pulse on (T ON ), pulse off time (T OFF ) and current (IP), whereas responses considered are material removal rate (MRR) and surface roughness. The experiments were carried according to a central composite rotatable design based on Response Surface Methodology (RSM). Analysis of variance (ANOVA) was used to study how input parameters affect output parameters. To obtain improved MRR and decreased surface roughness value, the output was optimised using Desirability Function Analysis (DFA). The results show that factors such as T ON , T OFF , and IP are significant variables that influenced MRR and Ra. When T ON and IP were increased, there was an increase in MRR and Ra, while when T OFF decreased, it decreased MRR and Ra. Optimisation results revealed that for MRR, optimum parameters are T ON set at 2 μs and T OFF set at 16 μs and IP by 187.272 A.