Intelligent tool wear monitoring based on parallel residual and stacked bidirectional long short-term memory network |
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| Affiliation: | 1. Key Laboratory of Advanced Manufacturing and Intelligent Technology, Ministry of Education, Harbin University of Science and Technology, Harbin, 150080, PR China;2. George W. Woodruff School of Mechanical Engineering, Georgia Institute of Technology, Atlanta, 30332, USA;1. Institute of Advanced Manufacturing Technology, Hefei Institutes of Physical Science, Chinese Academy of Science, Huihong Building, Changwu Middle Road 801, Changzhou 213164, Jiangsu, China;2. Institute of Precision Manufacturing, School of Machinery and Automation, Wuhan University of Science and Technology, #947 Heping Avenue, Qingshan District, Wuhan 430081, Hubei, China;1. Department of Automation, University of Science and Technology of China, Hefei 230026, Anhui, China;2. School of Mechanical and Electric Engineering, Soochow University, Suzhou 215021, Jiangsu, China;1. School of Mechanical and Transportation Engineering, China University of Petroleum, Beijing 102249, China;2. School of Electrical and Electronic Engineering, Nanyang Technological University, 639798, Singapore;1. College of Mechanical and Electrical Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China;2. School of Engineering, University of Greenwich, Chatham Maritime, Kent ME4 4TB, UK;3. Manufacturing Technology Centre, Ansty Park, Coventry CV7 9JU, UK |
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| Abstract: | Effective tool wear monitoring (TWM) is essential for accurately assessing the degree of tool wear and for timely preventive maintenance. Existing data-driven monitoring methods mainly rely on complex feature engineering, which reduces the monitoring efficiency. This paper proposes a novel TWM model based on a parallel residual and stacked bidirectional long short-term memory (PRes–SBiLSTM) network. First, a parallel residual network (PResNet) is used to extract the multi-scale local features of sensor signals adaptively. Subsequently, a stacked bidirectional long short-term memory (SBiLSTM) network is used to obtain the time-series features related to the tool wear characteristics. Finally, the predicted tool wear value is outputted through a fully connected network. A smoothing correction method is applied to improve the prediction accuracy. The proposed model is experimentally verified to have a high prediction accuracy without sacrificing its generalization ability. A TWM system framework based on the PRes–SBiLSTM network is proposed, which has a certain reference value for TWM in actual industrial environments. |
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| Keywords: | Tool wear Tool wear monitoring Deep learning Convolutional neural network Parallel residual network Bidirectional long short-term memory network |
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