Wear is one of the main forms of tool failure during machining. The prediction of tool wear is of great significance for ensuring the high quality of the workpiece. In order to improve prediction accuracy of tool wear, a tool wear prediction model based on singular value decomposition (SVD) and bidirectional long short-term memory neural network (BiLSTM) is proposed. The cutting force signal is taken as the monitoring signal. Firstly, the raw cutting force signal is reconstructed by Hankle matrix, and the SVD of the reconstructed matrix is performed to extract the signal features. Then, SVD features of the current sampling period and the previous four sampling periods are taken as the input, and the tool wear prediction value at the current time is obtained based on the BiLSTM. The experimental results show that the proposed SVD-BiLSTM model can effectively predict the tool wear and obtain higher prediction accuracy than other comparison models.
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This work was supported by the National Natural Science Foundation of China (Grant No. 51775374), Inner Mongolia Autonomous Region of Science and technology innovation guiding project KCBJ2018028, College Scientific Research Project of Inner Mongolia Autonomous Region NJZY18159, Natural Science Foundation of the Inner Mongolia Autonomous Region of China 2018MS05025, Program for Young Talents of Science and Technology in Universities of Inner Mongolia Autonomous Region NJYT-19-B15.
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Wu, X., Li, J., Jin, Y. et al. Modeling and analysis of tool wear prediction based on SVD and BiLSTM. Int J Adv Manuf Technol 106, 4391–4399 (2020). https://doi.org/10.1007/s00170-019-04916-3
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