In modern industrial machining processes, cutting tools play an important role, and their health directly affects the quality of machined parts. However, changing cutting tools based on experience not only increases costs but also reduces productivity. Therefore, predicting the future health of the cutting tools in advance can enable cutting tools changes to be carried out at the right time. The existing health status assessment of a cutting tools consists of three main areas: cutting tools wear prediction, health stages division, and reliability assessment. But traditional deep learning models usually process these three tasks separately, ignoring the correlation that exists between the three tasks. In order to solve this problem, this paper proposes a multi-task model based on temporal convolutional network (TCN) and progressive layered extraction (PLE) network. Firstly, the pre-processed data are subjected to feature extraction and feature selection and the redundant information between the features is reduced by using an autoencoder. Secondly, the TCN network module is used to extract the correlation feature information of multiple tasks in time, the PLE network module learns the difference information between each task, and finally, the prediction output is made by each sub-task module for each sub-task. Finally, the dynamic weight average method is used to adjust the weights of the loss function, which avoids manual adjustment of the weights. The experimental results show that the method not only achieves the prediction of multiple tasks but also has high prediction accuracy.
- Open Access
- Article
- Xiaoyu Yin 1,
- Xiaohua Li 2,
- Yong Zhang 1,
- Wei Zhou 1, *,
- Zhenxing Liu 1
Author Information
Received: 05 Mar 2025 | Accepted: 07 Mar 2025 | Published: 27 Jun 2025
Abstract
Keywords
multi-task learning | cutting tools wear prediction | health stages division | reliability assessment
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