Multi-objective optimization in wire-EDM process using grey relational analysis method (GRA) and backpropagation neural network–genetic algorithm (BPNN–GA) methods

• Published in: Multidiscipline modeling in materials and structures Vol. 15; no. 5; pp. 1016 - 1034
• Main Authors: Soepangkat, Bobby Oedy Pramoedyo, Norcahyo, Rachmadi, Rupajati, Pathya, Effendi, Mohammad Khoirul, Agustin, Helena Carolina Kis
• Format: Journal Article
• Language: English
• Published: Bingley Emerald Publishing Limited 02.09.2019; Emerald Group Publishing Limited
• Subjects: Algorithms; Artificial neural networks; Back propagation; Composite materials; Cooling; Drilling; Electric discharge machining; Electric potential; Genetic algorithms; Multiple objective analysis; Neural networks; Optimization; Orthogonal arrays; Parameter estimation; Process parameters; Structural steels; Surface cracks; Surface roughness; Taguchi methods; Thickness; Tool steels; Voltage; Wire; BPNN‒GA; SKD 61; GRA; Wire-EDM
• ISSN: 1573-6105; 1573-6113
• DOI: 10.1108/MMMS-06-2018-0112

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.