Tool Wear Prediction Model Using Multi-Channel 1D Convolutional Neural Network and Temporal Convolutional Network
Tool wear prediction can ensure product quality and production efficiency during manufacturing. Although traditional methods have achieved some success, they often face accuracy and real-time performance limitations. The current study combines multi-channel 1D convolutional neural networks (1D-CNNs)...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2024-01-01
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| Series: | Lubricants |
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| Online Access: | https://www.mdpi.com/2075-4442/12/2/36 |
| _version_ | 1797297673775087616 |
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| author | Min Huang Xingang Xie Weiwei Sun Yiming Li |
| author_facet | Min Huang Xingang Xie Weiwei Sun Yiming Li |
| author_sort | Min Huang |
| collection | DOAJ |
| description | Tool wear prediction can ensure product quality and production efficiency during manufacturing. Although traditional methods have achieved some success, they often face accuracy and real-time performance limitations. The current study combines multi-channel 1D convolutional neural networks (1D-CNNs) with temporal convolutional networks (TCNs) to enhance the precision and efficiency of tool wear prediction. A multi-channel 1D-CNN architecture is constructed to extract features from multi-source data. Additionally, a TCN is utilized for time series analysis to establish long-term dependencies and achieve more accurate predictions. Moreover, considering the parallel computation of the designed architecture, the computational efficiency is significantly improved. The experimental results reveal the performance of the established model in forecasting tool wear and its superiority to the existing studies in all relevant evaluation indices. |
| first_indexed | 2024-03-07T22:23:46Z |
| format | Article |
| id | doaj.art-61ca4ebce11b46c797e5e953e40a9528 |
| institution | Directory Open Access Journal |
| issn | 2075-4442 |
| language | English |
| last_indexed | 2024-03-07T22:23:46Z |
| publishDate | 2024-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Lubricants |
| spelling | doaj.art-61ca4ebce11b46c797e5e953e40a95282024-02-23T15:24:53ZengMDPI AGLubricants2075-44422024-01-011223610.3390/lubricants12020036Tool Wear Prediction Model Using Multi-Channel 1D Convolutional Neural Network and Temporal Convolutional NetworkMin Huang0Xingang Xie1Weiwei Sun2Yiming Li3School of Mechanical and Electrical Engineering, China University of Mining and Technology (Beijing), Beijing 100083, ChinaSchool of Mechanical and Electrical Engineering, China University of Mining and Technology (Beijing), Beijing 100083, ChinaMechanical Electrical Engineering School, Beijing Information Science and Technology University, Beijing 100192, ChinaMechanical Electrical Engineering School, Beijing Information Science and Technology University, Beijing 100192, ChinaTool wear prediction can ensure product quality and production efficiency during manufacturing. Although traditional methods have achieved some success, they often face accuracy and real-time performance limitations. The current study combines multi-channel 1D convolutional neural networks (1D-CNNs) with temporal convolutional networks (TCNs) to enhance the precision and efficiency of tool wear prediction. A multi-channel 1D-CNN architecture is constructed to extract features from multi-source data. Additionally, a TCN is utilized for time series analysis to establish long-term dependencies and achieve more accurate predictions. Moreover, considering the parallel computation of the designed architecture, the computational efficiency is significantly improved. The experimental results reveal the performance of the established model in forecasting tool wear and its superiority to the existing studies in all relevant evaluation indices.https://www.mdpi.com/2075-4442/12/2/36tool wear predictionone-dimensional convolutiontemporal convolutional network |
| spellingShingle | Min Huang Xingang Xie Weiwei Sun Yiming Li Tool Wear Prediction Model Using Multi-Channel 1D Convolutional Neural Network and Temporal Convolutional Network Lubricants tool wear prediction one-dimensional convolution temporal convolutional network |
| title | Tool Wear Prediction Model Using Multi-Channel 1D Convolutional Neural Network and Temporal Convolutional Network |
| title_full | Tool Wear Prediction Model Using Multi-Channel 1D Convolutional Neural Network and Temporal Convolutional Network |
| title_fullStr | Tool Wear Prediction Model Using Multi-Channel 1D Convolutional Neural Network and Temporal Convolutional Network |
| title_full_unstemmed | Tool Wear Prediction Model Using Multi-Channel 1D Convolutional Neural Network and Temporal Convolutional Network |
| title_short | Tool Wear Prediction Model Using Multi-Channel 1D Convolutional Neural Network and Temporal Convolutional Network |
| title_sort | tool wear prediction model using multi channel 1d convolutional neural network and temporal convolutional network |
| topic | tool wear prediction one-dimensional convolution temporal convolutional network |
| url | https://www.mdpi.com/2075-4442/12/2/36 |
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