绿色电火花成型加工多目标工艺参数优化  

Optimization of Multi-objective Process Parameters in Green-EDM Process

作　　者：明五一[1] 沈帆 何文斌[1] 陈志君 MING Wuyi;SHEN Fan;HE Wenbin;CHEN Zhijun(Zhengzhou University of Light Industry,Zhengzhou Henan 450002,China;Guangdong Provincial Key Laboratory of Manufacturing Equipment Digitization,Guangdong Industrial Technology Research Institute of Huazhong University of Science and Technology,Dongguan Guangdong 523808,China)

机构地区：[1]郑州轻工业学院,河南郑州450002 [2]广东华中科技大学工业技术研究院,广东省制造装备数字化重点实验室,广东东莞523808

出　　处：《机床与液压》2020年第1期23-28,共6页Machine Tool & Hydraulics

基　　金：2018河南省自然科学基金资助项目(182300410170,182300410215);广东省制造装备数字化重点实验室开放项目(2017B030314146)。

摘　　要：为实现电火花成型加工的绿色制造,在保证加工效率和加工质量的基础上尽可能减少能耗和污染物排放,采用正交试验与非支配排序遗传算法(NSGA-Ⅱ)对加工参数进行多目标优化。选择脉冲电流(I)、周率(T)及效率(η)3个工艺参数作为因变量,表面粗糙度(Ra)、能耗(EEV)和环境污染物(EEC)作为响应值,对SKD11进行电火花成型加工试验。通过回归分析验证工艺参数与响应之间所建模型的正确性,并利用信噪比分析获得影响能耗和污染物排放的主要因素。得出了加工工艺参数与加工效果之间的回归关系,并通过NSGA-Ⅱ算法对其进行优化得到Pareto前沿。Ra、EEV和EEC预测结果的平均相对误差分别为6.46%、10.45%、9.58%,表明优化结果准确有效,对今后的研究以及企业的绿色加工具有一定参考意义。In order to realize green manufacturing of Electrical Discharge Machining(EDM), energy consumption and pollutant emission are reduced as much as possible on the basis of ensuring machining efficiency and quality. Orthogonal experiment and non-dominated sorting genetic algorithm(NSGA-Ⅱ) were used to optimize the machining parameters. The parameters of current(I), cycle rate(T) and efficiency(η) were selected as dependent variables, surface roughness(Ra), energy consumption(EEV) and environment contamination(EEC) as evaluating indicator, and the EDM experiments of SKD11 were carried out. The correctness of the model between process parameters and response was verified by regression analysis, and the main factors affecting energy consumption and pollutant emission were obtained by signal-to-noise ratio analysis. Finally, the regression relationship between the processing parameters and the machining effect was obtained.The NSGA-II algorithm was used to optimize the parameters and got the Pareto frontier. The average relative errors of Ra, EEV and EEC prediction results are 6.46%, 10.45% and 9.58%, respectively, which shows that the optimization results are accurate and effective. Therefore, there is a certain guiding significance for research and green processing of enterprises.

关 键 词：绿色制造 电火花成型加工 回归分析 NSGA-Ⅱ算法 

分 类 号：TG661[金属学及工艺—金属切削加工及机床]