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首页> 外文期刊>Arabian Journal for Science and Engineering >RSM- and NSGA-II-Based Multiple Performance Characteristics Optimization of EDM Parameters for AISI 5160
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RSM- and NSGA-II-Based Multiple Performance Characteristics Optimization of EDM Parameters for AISI 5160

机译:基于RSM和NSGA-II的AISI 5160 EDM参数的多种性能特征优化

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In this paper, different methods of flushing are used to study the machining characteristics of electric discharge machining (EDM). Flushing mode plays an important role in any EDM operation. An incorrect flushing can result in erratic cutting and poor machining conditions. In present work, AISI 5160 alloy steel is selected as a work material to investigate the performance of EDM. Two of the most important response variables, surface roughness (SR) and material removal rate (MRR), are selected to compute the influence of input control factors. Central composite design is used for experimentation planning using response surface methodology. Analysis of variance is utilized to investigate the influence of control factors on response. Mathematical models are solved with the help of non-dominating sorting genetic algorithm II (NSGA-II). Validation experiments confirmed that at the optimal levels of process parameters the predicted values of MRR and SR were 1.167 g/min and , respectively. The predicted values of MRR and SR suggested by NSGA-II give a better correlation with experimental values, i.e., 1.149 g/min and , respectively.
机译:在本文中,使用不同的冲洗方法来研究放电加工(EDM)的加工特性。冲洗模式在任何EDM操作中都起着重要作用。不正确的冲洗会导致切割不稳定和加工条件差。在目前的工作中,选择AISI 5160合金钢作为工作材料来研究EDM的性能。选择两个最重要的响应变量,即表面粗糙度(SR)和材料去除率(MRR),以计算输入控制因素的影响。中央复合设计用于使用响应面方法进行实验计划。方差分析用于调查控制因素对响应的影响。数学模型借助非主导排序遗传算法II(NSGA-II)进行求解。验证实验证实,在最佳工艺参数水平下,MRR和SR的预测值分别为1.167 g / min和。 NSGA-II提出的MRR和SR的预测值与实验值具有更好的相关性,分别为1.149 g / min和。

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