Tool wear condition monitoring in milling process based on data fusion enhanced long short-term memory network under different cutting conditions

• Autorzy: Zheng Guoxiao , Sun Weifang , Zhang Hao , Zhou Yuqing , Gao Chen

Identyfikatory

DOI

10.17531/ein.2021.4.3

• Języki publikacji: EN

Abstrakty

EN

Tool wear condition monitoring (TCM) is essential for milling process to ensure the machining quality, and the long short-term memory network (LSTM) is a good choice for predicting tool wear value. However, the robustness of LSTM- based method is poor when cutting condition changes. A novel method based on data fusion enhanced LSTM is proposed to estimate tool wear value under different cutting conditions. Firstly, vibration time series signal collected from milling process are transformed to feature space through empirical mode decomposition, variational mode decomposition and fourier synchro squeezed transform. And then few feature series are selected by neighborhood component analysis to reduce dimension of the signal features. Finally, these selected feature series are input to train the bidirectional LSTM network and estimate tool wear value. Applications of the proposed method to milling TCM experiments demonstrate it outperforms significantly SVR- based and RNN- based methods under different cutting conditions.

Słowa kluczowe

EN

tool wear condition monitoring; empirical mode decomposition; variational mode decomposition; fourier synchro squeezed transform; neighborhood component analysis; long short-term memory network

• Wydawca: Polskie Naukowo-Techniczne Towarzystwo Eksploatacyjne
• Czasopismo: Eksploatacja i Niezawodność
• Rocznik: 2021
• Tom: Vol. 23, no. 4
• Strony: 612--618
• Opis fizyczny: Bibliogr. 35 poz., rys., tab.

Twórcy

autor

Zheng Guoxiao

• College of Mechanical and Electrical Engineering, Wenzhou University, Wenzhou, China, 325035

autor

Sun Weifang

• College of Mechanical and Electrical Engineering, Wenzhou University, Wenzhou, China, 325035

autor

Zhang Hao

• Shaoxing Customs, Shaoxing, China, 312099

autor

Zhou Yuqing

• College of Mechanical and Electrical Engineering, Wenzhou University, Wenzhou, China, 325035

autor

Gao Chen

• School of Mechatronics and Transportation, Jiaxing Nanyang Polytechnic Institute, Jiaxing, China, 314031

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