Wear Prediction of Tool Based on Modal Decomposition and MCNN-BiLSTM

He, Zengpeng, Liu, Yefeng, Pang, Xinfu and Zhang, Qichun (2023) Wear Prediction of Tool Based on Modal Decomposition and MCNN-BiLSTM. Processes, 11 (10).

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Abstract

Metal cutting is a complex process with strong randomness and nonlinear characteristics in its dynamic behavior, while tool wear or fractures will have an immediate impact on the product surface quality and machining precision. A combined prediction method comprising modal decomposition, multi-channel input, a multi-scale Convolutional neural network (CNN), and a bidirectional long-short term memory network (BiLSTM) is presented to monitor tool condition and to predict tool-wear value in real time. This method considers both digital signal features and prediction network model problems. First, we perform correlation analysis on the gathered sensor signals using Pearson and Spearman techniques to efficiently reduce the amount of input signals. Second, we use Complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) to enhance the local characteristics of the signal, then boost the neural network’s identification accuracy. In addition, the deconstructed signal is converted into a multi-channel input matrix, from which multi-scale spatial characteristics and two-way temporal features are recovered using multi-scale CNN and BiLSTM, respectively. Finally, this strategy is adopted in simulation verification using real PHM data. The wear prediction experimental results show that, in the developed model, C1, C4, and C6 have good prediction performance, with RMSE of 8.2968, 12.8521, 7.6667, and MAE of 6.7914, 9.9263, and 5.9884, respectively, significantly lower than SVR, B-BiLSTM, and 2DCNN models.

Item Type:	Article
Additional Information:	Publisher Copyright: © 2023 by the authors.
Keywords:	bidirectional short-long term memory neural network, distributed convolutional neural network, modal decomposition, tool wear prediction
Depositing User:	RED Unit Admin
Date Deposited:	27 May 2025 13:23
Last Modified:	27 May 2025 13:23
URI:	https://bnu.repository.guildhe.ac.uk/id/eprint/20258

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