Tool Wear Prediction Based on Adaptive Feature and Temporal Attention with Long Short-Term Memory Model

Wanzhen Wang; Sze Song Ngu; Miaomiao Xin; Rong Liu; Qian Wang; Man Qiu; Shengqun Zhang

doi:10.46604/ijeti.2024.13387

Authors

Wanzhen Wang Department of Intelligent Manufacturing and Control Engineering, Qilu Institute of Technology, Jinan, China; Faculty of Engineering, Universiti Malaysia Sarawak, Kota Samarahan, Sarawak, Malaysia
Sze Song Ngu Faculty of Engineering, Universiti Malaysia Sarawak, Kota Samarahan, Sarawak, Malaysia
Miaomiao Xin Department of Intelligent Manufacturing and Control Engineering, Qilu Institute of Technology, Jinan, China; Faculty of Engineering, Universiti Malaysia Sarawak, Kota Samarahan, Sarawak, Malaysia
Rong Liu Department of Intelligent Manufacturing and Control Engineering, Qilu Institute of Technology, Jinan, China; Faculty of Engineering, Universiti Malaysia Sarawak, Kota Samarahan, Sarawak, Malaysia
Qian Wang Faculty of Engineering, Universiti Malaysia Sarawak, Kota Samarahan, Sarawak, Malaysia
Man Qiu Faculty of Engineering, Universiti Malaysia Sarawak, Kota Samarahan, Sarawak, Malaysia
Shengqun Zhang Department of Intelligent Manufacturing and Control Engineering, Qilu Institute of Technology, Jinan, China; Faculty of Engineering, Universiti Malaysia Sarawak, Kota Samarahan, Sarawak, Malaysia

DOI:

https://doi.org/10.46604/ijeti.2024.13387

Keywords:

feature attention, temporal attention, tool wear, LSTM

Abstract

Effective monitoring of tool wear status can improve productivity and reduce losses. In previous studies, extensive feature selection was required when using the traditional machine learning method. The gating mechanism in the traditional long short-term memory (LSTM) model may incur information loss and a weaker representation of global sequential dependencies in handling long sequences. This paper aims to enhance the performance of the LSTM model in tool wear prediction by combining feature and temporal attention. Firstly, the original vibration signal is divided into sub-sequences and related features extracted. Secondly, the ability to capture global sequential dependencies using the LSTM model is improved by feature and temporal attention. Finally, a fully connected layer is used to predict tool wear values. Compared to traditional LSTM, the proposed method performs best in three evaluation metrics, RMSE, MAE, and the coefficient of determination.

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