Wire EDM Parameter Optimization of AlSi10Mg Alloy

This work attempts to optimize the multi-objective characteristics of wire electrical discharge machining (WEDM) in SLM-fabricated AlSi10Mg through a hybrid artificial neural network (ANN) coupled genetic algorithm approach. The SLM is unambiguously one most effective commercially viable successful additive manufacturing (AM) technologies that have potential replace many traditional methods manufacturing. However, highly intricate metallic support structures created are too strong be eliminated by hands for which precision operations such as WEDM widely employed post-processing SLM–AlSi10Mg. Taguchi experimental design, considering three influencing factors, performed obtain micro-hardness and surface roughness results. input factors optimization current, voltage, pulse time-on process. using ANN GA approach where model has been generated first results best fed optimization. For this, five variants three-layered, multi-perceptron models with feed-forward (BP) also developed. current supplemented Levenberg–Marquardt uses logarithmic sigmoid (logsig) linear (purelin) transfer functions. Finally, response values from (3–10–2) GA. present study establishes following optimized process parameters: 12 A 42 V µs maximized 478 VHN minimized 4.3 µm, both greater than 98% confidence level. reports briefly phase characterization presence Si particles, α-Al, Mg2Si phases on recast surface. quality specimen exhibits superior its other counterparts.