Tool condition monitoring in milling process using multifractal detrended fluctuation analysis and support vector machin
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ORIGINAL ARTICLE
Tool condition monitoring in milling process using multifractal detrended fluctuation analysis and support vector machine Jingchao Guo 1,2 & Anhai Li 1,2
&
Rufeng Zhang 1,2
Received: 10 April 2020 / Accepted: 9 August 2020 / Published online: 24 August 2020 # Springer-Verlag London Ltd., part of Springer Nature 2020
Abstract Tool wear will lead to the reduction of surface quality and machining accuracy. Therefore, tool condition monitoring is vital to the improvement of industrial production efficiency and quality. In this paper, first of all, the internal mechanism of the milling process and the performance characteristics of the milling signals were analyzed. It is found that there are different trends between the large and small fluctuations of milling signals in the process of tool wear increasing. This property can be characterized by various parameters of multifractal spectrum to establish the relationship between tool wear and multifractal parameters. By analyzing the changes of multifractal spectrum parameters, the tool wear monitoring can be realized. Then, the multifractal detrended fluctuation analysis (MFDFA) method is used to calculate the mean square error, generalized Hurst exponent, and multifractal spectrum parameters, which are the eigenvectors, and establish its relationship with tool wear. Finally, the tool condition diagnosis is conducted by a support vector machine (SVM). The results show that the tool condition monitoring method of MFDFA combined with SVM is proved to be effective and the multifractal parameters of MFDFA are very sensitive to tool wear. Keywords Tool condition monitor . Cutting force signal . Vibration signal . Multifractal detrended fluctuation analysis . Support vector machine
1 Introduction Tool wear is the result of mechanical friction, cutting force, and cutting temperature in milling process. It is a very complex phenomenon of physical and chemical changes, which consists of abrasion, adhesion, diffusion, fatigue, and chemical wear [1]. In the process of cutting, the tool contacts with the workpiece directly. The tool wear will increase the roughness of the workpiece surface and reduce the quality of the workpiece. Serious tool wear will cause tool chipping, fracture, and chatter, which will damage the workpiece and machine tool and cause serious processing accidents [2]. Therefore, in order to obtain better surface quality and reduce * Anhai Li [email protected] 1
Key Laboratory of High Efficiency and Clean Mechanical Manufacture of MOE, School of Mechanical Engineering, Shandong University, Jinan 250061, China
2
National Demonstration Center for Experimental Mechanical Engineering Education, Shandong University, Jinan 250061, China
the loss caused by tool wear, the research of signal processing and pattern recognition technology for tool condition monitoring has become an urgent problem to be solved [3]. Tool condition monitoring can be divided into direct methods and indirect methods based on the method of measurement technique and complexit
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