Multiparametric Optimization and Prediction of Tool Steel Machined Surface Quality in Accurate Wire Electrical Discharge Machining
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Abstract
This article explains the multiparametric optimization of machined surface quality and provides mathematical models that can predict the high productivity of the WEDM process for tool steels as well as the superior quality of a precisely machined surface. The experimental study was carried out using four technological factors and the full DoE factorial design method. The output quantitative parameter Material Removal Rate (MRR) and the observed output qualitative parameter Surface Roughness (SR) were evaluated using Grey Relational Analysis (GRA) and Analysis of Variance (ANOVA). To represent the diverse responses of the tool steels being studied, Multiple Regression Models (MRM) were developed using a regression tool set. Although the parameters were linked to the positive outcomes of the output-dependent parameters SR and MRR.The multiparametric optimization findings demonstrated a link between the input variable parameters of the electrical discharge process in the case of low peak current I, low value of pulse on-time duration ton, low voltage of discharge U, and high value of pulse off-time duration toff. The multiparametric optimization produced significant results that demonstrated the reciprocal dependence between the observed output process parameters. An ideal SR value of 1.50 μm and an MRR value of 12.50 mm3·min−1 were obtained using L8-level settings using the input variable parameters I, ton, U, and toff (2 A, 32 μs, 90 V, and 20 μs, respectively).
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