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In this study, the optimization of recast layer thickness and surface roughness (SR) simultaneously in a Wire-EDM process by using Taguchi method with fuzzy logic has been applied. The Wire-EDM process parameters (arc on time, on time, open voltage, off time and servo voltage) were optimized with considerations of multiple performance characteristics, i.e., recast layer thickness and SR. Based on the Taguchi method, an L18 mixed-orthogonal array table was chosen for the experiments. Fuzzy reasoning of the multiple performance characteristics has been developed based on fuzzy logic, which then converted into a fuzzy reasoning grade or FRG. As a result, the optimization of complicated multiple performance characteristics was transformed into the optimization of single response performance index. Experimental results have shown that machining performance characteristics of Wire-EDM process can be improved effectively through the combination of Taguchi method and fuzzy logic.

In this paper, the optimization of surface roughness and recast layer thickness of a WEDM process of AISI D2 steel was investigated by using Taguchi method, grey relational analysis and fuzzy logic. The experiments were conducted under varying flushing pressure, on time, open voltage, off time and servo voltage. An orthogonal array, signal-to-noise (S/N) ratio, grey relational analysis, grey-fuzzy reasoning grade and analysis of variance were employed to the study of the multiple performance characteristics. Experimental results have shown that machining performance characteristics in WEDM process of AISI D2 steel can be improved effectively through the combination of Taguchi method, grey relational analysis and fuzzy logic.

Purpose The purpose of this paper is to investigate prediction and optimization of multiple performance characteristics in the wire electrical discharge machining (wire-EDM) process of SKD 61 (AISI H13) tool steel. Design/methodology/approach The experimental studies were conducted under varying wire-EDM process parameters, which were arc on time, on time, open voltage, off time and servo voltage. The optimized responses were recast layer thickness (RLT), surface roughness (SR) and surface crack density (SCD). Arc on time was set at two different levels, whereas the other four parameters were set at three different levels. Based on Taguchi method, an L18 mixed-orthogonal array was selected for the experiments. Further, three methods, namely grey relational analysis (GRA), backpropagation neural network (BPNN) and genetic algorithm (GA), were applied separately. GRA was performed to obtain a rough estimation of optimum drilling parameters. The influences of drilling parameters on multiple performance characteristics were determined by using percentage contributions. BPNN architecture was determined to predict the multiple performance characteristics. GA method was then applied to determine the optimum wire-EDM parameters. Findings The minimum RLT, SR and SCD could be obtained by setting arc on time, on time, open voltage, off time and servo voltage at 2 ms, 3 ms, 90 volt, 10 ms and 38 volt, respectively. The experimental confirmation results showed that BPNN-based GA optimization method could accurately predict and significantly improve all of the responses. Originality/value There were no publications regarding multi-response optimization using a combination of GRA and BPNN-based GA methods during wire-EDM process available.