Abstract

A systematic read on evaluating the machining characteristics of wire cut electrical discharge machining (WEDM) using analysis of variance (ANOVA) and multilinear regression model based multi objective optimization is provided during this analysis article. The current work explores, the surface roughness of copper metal matrix composite (MMCs) is minimized by optimizing the process parameters like spark on time, spark off time, peak current and wire feed. Taguchi’s L9 orthogonal array has been used to conduct the experiments for two samples having a different composition to measure the surface roughness. The order of significance of parameters on surface roughness (Ra) for sample 1 and 2 MMCs were found using ANOVA and a multilinear regression model (MLRM) was developed to predict the Ra value. The new contribution in the present work is that, the coefficients of MLRM were optimized using artificial immune system algorithm with the objective of minimizing the mean absolute percentage error. Finally, the optimum process parameters were obtained to minimize the surface roughness and reported that the reduced value of Ra for 2.5% and 5% WC Copper MMCs were 0.9 μm and 1.1 μm, respectively later which were established by confirmation experiments.

摘要

在这篇分析文章中，系统地介绍了使用方差分析（ANOVA）和基于多线性回归模型的多目标优化评估线切割放电加工（WEDM）的加工特性。当前的工作是探索，通过优化工艺参数（如火花接通时间，火花断开时间，峰值电流和送丝）来最小化铜金属基复合材料（MMC）的表面粗糙度。Taguchi的L9正交阵列已用于对具有不同成分的两个样品进行表面粗糙度测量。使用方差分析发现了样品1和2 MMC的参数对表面粗糙度（Ra）的重要性顺序，并开发了多线性回归模型（MLRM）来预测Ra值。当前工作的新贡献是，为了使平均绝对百分比误差最小，采用人工免疫系统算法对MLRM系数进行了优化。最后，获得了最佳工艺参数以最小化表面粗糙度，并报告了2.5％和5％WC铜MMC的Ra降低值分别为0.9μm和1.1μm，随后通过确认实验确定。