Tool Condition Monitoring and Remaining Useful Life Prognostic Based on a Wireless Sensor in Dry Milling Operations.
Tool breakage causes losses of surface polishing and dimensional accuracy for machined part, or possible damage to a workpiece or machine. Tool Condition Monitoring (TCM) is considerably vital in the manufacturing industry. In this paper, an indirect TCM approach is introduced with a wireless triaxial accelerometer. The vibrations in the three vertical directions (x, y and z) are acquired during milling operations, and the raw signals are de-noised by wavelet analysis. These features of de-noised signals are extracted in the time, frequency and time-frequency domains. The key features are selected based on Pearson's Correlation Coefficient (PCC). The Neuro-Fuzzy Network (NFN) is adopted to predict the tool wear and Remaining Useful Life (RUL). In comparison with Back Propagation Neural Network (BPNN) and Radial Basis Function Network (RBFN), the results show that the NFN has the best performance in the prediction of tool wear and RUL.
School of Mechanical and Automotive Engineering, South China University of Technology, Guangzhou 510640, China. zhang.cunji@mail.scut.edu.cn.|Department of Information Engineering, Guangxi College of Water Resources and Electric Power, Nanning 530023, China. zhang.cunji@mail.scut.edu.cn.
Cunji
C
Zhang
School of Mechanical and Automotive Engineering, South China University of Technology, Guangzhou 510640, China. mexfyao@scut.edu.cn.
Xifan
X
Yao
School of Mechanical and Automotive Engineering, South China University of Technology, Guangzhou 510640, China. xtwgiqqk@163.com.
Jianming
J
Zhang
College of Engineering, South China Agricultural University, Guangzhou 510642, China. hjin@scau.edu.cn.
Hong
H
Jin
neuro-fuzzy network (NFN); remaining useful life (RUL); tool condition monitoring (TCM); wavelet analysis; wavelet packet transform (WPT); wireless sensor