Monitoring tool wear using wavelet package decomposition and a novel gravitational search algorithm-least square support vector machine model

作     者：Kong, Dongdong Chen, Yongjie Li, Ning 

作者机构：Huazhong Univ Sci & Technol Sch Mech Sci & Engn 1037 Luoyu Rd Wuhan Hubei Peoples R China 

出 版 物：《PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART C-JOURNAL OF MECHANICAL ENGINEERING SCIENCE》 (机械工程师学会会报；C辑：机械工程学杂志)

年 卷 期：2020年第234卷第3期

页      面：822-836页

核心收录：

学科分类：08[工学] 0802[工学-机械工程] 

基　　金：863 National High-Tech Research and Development Program of China [2013AA041108] 

主　　题：Tool wear estimation cutting forces wavelet package decomposition least square support vector machine gravitational search algorithm 

摘      要：Monitoring tool wear has drawn much attention recently since tool failure will make it hard to guarantee the surface integrity of workpieces and the stability of manufacturing process. In this paper, the integrated approach that combines wavelet package decomposition, least square support vector machine, and the gravitational search algorithm is proposed for monitoring the tool wear in turning process. Firstly, the wavelet package decomposition is utilized to decompose the original cutting force signals into multiple sub-bands. Root mean square of the wavelet packet coefficients in each sub-band are extracted as the monitoring features. Then, the gravitational search algorithm-least square support vector machine model is constructed by using the extracted wavelet-domain features so as to identify the tool wear states. Eight sets of cutting experiments are conducted to prove the superiority of the proposed integrated approach. The experimental results show that the wavelet-domain features can help to ameliorate the performance of the gravitational search algorithm-least square support vector machine model. Besides, gravitational search algorithm-least square support vector machine performs better than gravitational search algorithm-support vector machine in prediction accuracy of tool wear states even in the case of small-sized training data set and the time consumption of parameters optimization in gravitational search algorithm-least square support vector machine is less than that of gravitational search algorithm-support vector machine under large-sized training data set. What s more, the gravitational search algorithm-least square support vector machine model outperforms some other related methods for tool wear estimation, such as k-NN, feedforward neural network, classification and regression tree, and linear discriminant analysis.