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Tool wear condition monitoring in milling process based on data fusion enhanced long short-term memory network under different cutting conditions
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College of Mechanical and Electrical Engineering, Wenzhou University, Wenzhou, China, 325035
Shaoxing Customs, Shaoxing, China, 312099
School of Mechatronics and Transportation, Jiaxing Nanyang Polytechnic Institute, Jiaxing, China, 314031
Publication date: 2021-12-31
Eksploatacja i Niezawodność – Maintenance and Reliability 2021;23(4):612-618
  • A data fusion- LSTM is proposed to estimate tool wear under different cutting conditions.
  • NCA is used to select useful features fusioned by EMD VMD and FSST.
  • Experimental results show the proposed method outperforms significantly SVR and RNN.
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.
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