Tool wear state recognition based on GWO–SVM with feature selection of genetic algorithm
Tool wear is an important consideration for Computerized Numerical Control (CNC) machine tools as it directly affects machining precision. To realize the online recognition of tool wear degree, this research develops a tool wear monitoring system using an indirect measurement method which selects signal characteristics that are strongly correlated with tool wear to recognize tool wear status. The system combines support vector machine (SVM) and genetic algorithm (GA) to establish a nonlinear mapping relationship between a sample of cutting force sensor signal and tool wear level. The cutting force signal is extracted using time domain statistics, frequency domain analysis, and wavelet packet decomposition. GA is employed to select the sensitive features which have a high correlation with tool wear states. SVM is also applied to obtain the state recognition results of tool wear. The gray wolf optimization (GWO) algorithm is used to optimize the SVM parameters and to improve prediction accuracy and reduce internal parameters’ adjustment time. A milling experiment on AISI 1045 steel showed that when comparing with SVM optimized by commonly used optimization algorithms (grid search, particle swarm optimization, and GA), the proposed tool wear monitoring system can accurately reflect the degree of tool wear and achieves strong generalizability. A set of vibration signals are adopted to verify the presented research. Results show that the proposed tool wear monitoring system is robust.
KeywordsTool wear monitoring Feature selection Support vector machine Parameter optimization
This study was financially supported by the National Natural Science Foundation of China (No. 51665005), Innovation Project of Guangxi Graduate Education (YCBZ2017015), High-level Research Project of Qinzhou University (Grant no. 16PYSJ06), the Project of Guangxi Colleges and Universities Key Laboratory Breeding Base of Coastal Mechanical Equipment Design, Manufacturing and Control (GXLH2016ZD-06), and Guangxi Key Laboratory of manufacturing systems and advanced manufacturing technology.
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