Modeling and sensitivity analyzing of energy consumption in CNC face milling using optimized SVM approach
Currently,with global awareness of environmental protection as well as the pressing needs to increase efficiency,green manufacturing attracts more and more attentions.Determining the optimum input parameter settings(cutting speed,depth of cut and feed rate)in CNC face milling process can improve efficiency and reduce power consumption.This paper proposes a new method to achieve face milling energy consumption prediction based on supported vector machine(SVM)approach and harmony search algorithm.Firstly,the SVM model is constructed and trained for face milling process prediction based on the features extracted from testing data.Then a modified harmony search algorithm(MHS)is presented to optimize the SVM parameters.To verify the effectiveness of the proposed methodology,a case study has been conducted.The experiment results demonstrate that the SVM model can achieve higher accuracy and lower energy consumption compared with the automated neural networks search(ANS)in manufacturing process.Moreover,the global sensitivity analysis reveals that the cutting speed is the dominant factor for energy consumption in face milling process.
Support vector machine Energy consumption Modeling and impact factors analysis Face milling Harmony search algorithm
Wu Jiayang Gao Liang Yi Jin Lin Bowen
State Key Laboratory of Digital Manufacturing Equipment and Technology School of Mechanical Science and Engineering,Huazhong University of Science and Technology,Wuhan 430074,China
国际会议
第十七届国际制造会议(The 17th International Manufacturing Conference in China)(IMCC 2017))
深圳
英文
1-6
2017-11-23（万方平台首次上网日期，不代表论文的发表时间）