Adaptive Variational Modal Decomposition–Dual Attention Mechanism Parallel Residual Network: A Tool Lifetime Prediction Method Based on Adaptive Noise Reduction

Jing Kang, Taiyong Wang, Yi Li, Ye Wei, Yaomin Zhang and Ying Tian ()
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Jing Kang: School of Mechanical Engineering, Tianjin University, Tianjin 300354, China
Taiyong Wang: School of Mechanical Engineering, Tianjin University, Tianjin 300354, China
Yi Li: School of Mechanical Engineering, Tianjin University, Tianjin 300354, China
Ye Wei: School of Mechanical Engineering, Tianjin University, Tianjin 300354, China
Yaomin Zhang: School of Mechanical Engineering, Tianjin University, Tianjin 300354, China
Ying Tian: School of Mechanical Engineering, Tianjin University, Tianjin 300354, China

Mathematics, 2024, vol. 13, issue 1, 1-19

Abstract: This paper addresses the issue of noise interference and variable working conditions in the production and machining environment, which lead to weak tool life features and reduced prediction accuracy. A tool lifetime prediction method based on AVMD-DAMResNet is proposed. The method first adapts the parameters of the variational modal noise reduction algorithm using an improved sparrow optimization algorithm, and then reconstructs the original vibration signal with noise reduction. Second, the residual module of the deep residual network is enhanced using a two-dimensional attention mechanism. A parallel residual network tool prediction model (DAMResNet) was constructed to optimize the model’s weight allocation to different features, achieving multi-channel and multi-dimensional feature fusion. Finally, the noise-reduced signal was input into the DAMResNet model to accurately predict tool lifetime. The experimental results show that, compared with the original ResNet model, the proposed AVMD-DAMResNet model improves the coefficient of determination (R 2 ) by 5.8%, reduces the root mean square error (RMSE) by 31.2%, and decreases the mean absolute percentage error (MAPE) by 31.4%. These results demonstrate that the AVMD-DAMResNet-based tool lifetime prediction method effectively reduces noise and achieves high prediction accuracy.

Keywords: tool lifetime prediction; signal denoising; variational modal decomposition; residual neural network (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2024
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