Identification of Tool-Wear State Using Information Fusion and SSA–BP Neural Network
In modern manufacturing, cutting tools are essential for cutting processes, and their wear state directly affects the processing accuracy, production efficiency, and product quality. Identification of the tool-wear state using a single sensor is insufficient to satisfy the requirements of high-preci...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-03-01
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| Series: | Machines |
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| Online Access: | https://www.mdpi.com/2075-1702/13/4/256 |
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| author | Zishuo Wang Hongwei Cui Shuning Liang Tao Ding Xingquan Gao |
| author_facet | Zishuo Wang Hongwei Cui Shuning Liang Tao Ding Xingquan Gao |
| author_sort | Zishuo Wang |
| collection | DOAJ |
| description | In modern manufacturing, cutting tools are essential for cutting processes, and their wear state directly affects the processing accuracy, production efficiency, and product quality. Identification of the tool-wear state using a single sensor is insufficient to satisfy the requirements of high-precision, high-efficiency machining. To address this problem, this paper proposes a novel approach to identify the tool-wear state using information fusion technology and the sparrow search algorithm (SSA)–backpropagation (BP) neural network framework. This method uses a principal component analysis (PCA) to fuse multi-domain features extracted from three-way vibration signals, power signals, and temperature signals. Subsequently, the optimal initial threshold and weight of the BP neural network are optimized using the SSA to prevent the network from falling into the local optimum and accelerate the convergence of the algorithm. Lastly, a tool-wear-state identification model based on the SSA–BP neural network is constructed. Experimental results show that the proposed method has an identification accuracy of 98.33%, precision rate of 98.81%, recall rate of 97.96%, and F1 score of 98.36%. |
| format | Article |
| id | doaj-art-b58897825bc1464abd16cf5b35855a19 |
| institution | OA Journals |
| issn | 2075-1702 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Machines |
| spelling | doaj-art-b58897825bc1464abd16cf5b35855a192025-08-20T02:28:24ZengMDPI AGMachines2075-17022025-03-0113425610.3390/machines13040256Identification of Tool-Wear State Using Information Fusion and SSA–BP Neural NetworkZishuo Wang0Hongwei Cui1Shuning Liang2Tao Ding3Xingquan Gao4School of Information and Control Engineering, Jilin Institute of Chemical Technology, Jilin 132022, ChinaSchool of Information and Control Engineering, Jilin Institute of Chemical Technology, Jilin 132022, ChinaSchool of Information and Control Engineering, Jilin Institute of Chemical Technology, Jilin 132022, ChinaSchool of Information and Control Engineering, Jilin Institute of Chemical Technology, Jilin 132022, ChinaSchool of Information and Control Engineering, Jilin Institute of Chemical Technology, Jilin 132022, ChinaIn modern manufacturing, cutting tools are essential for cutting processes, and their wear state directly affects the processing accuracy, production efficiency, and product quality. Identification of the tool-wear state using a single sensor is insufficient to satisfy the requirements of high-precision, high-efficiency machining. To address this problem, this paper proposes a novel approach to identify the tool-wear state using information fusion technology and the sparrow search algorithm (SSA)–backpropagation (BP) neural network framework. This method uses a principal component analysis (PCA) to fuse multi-domain features extracted from three-way vibration signals, power signals, and temperature signals. Subsequently, the optimal initial threshold and weight of the BP neural network are optimized using the SSA to prevent the network from falling into the local optimum and accelerate the convergence of the algorithm. Lastly, a tool-wear-state identification model based on the SSA–BP neural network is constructed. Experimental results show that the proposed method has an identification accuracy of 98.33%, precision rate of 98.81%, recall rate of 97.96%, and F1 score of 98.36%.https://www.mdpi.com/2075-1702/13/4/256tool-wear-state identificationinformation fusionSSA–BP neural networkPCAfeature extraction |
| spellingShingle | Zishuo Wang Hongwei Cui Shuning Liang Tao Ding Xingquan Gao Identification of Tool-Wear State Using Information Fusion and SSA–BP Neural Network Machines tool-wear-state identification information fusion SSA–BP neural network PCA feature extraction |
| title | Identification of Tool-Wear State Using Information Fusion and SSA–BP Neural Network |
| title_full | Identification of Tool-Wear State Using Information Fusion and SSA–BP Neural Network |
| title_fullStr | Identification of Tool-Wear State Using Information Fusion and SSA–BP Neural Network |
| title_full_unstemmed | Identification of Tool-Wear State Using Information Fusion and SSA–BP Neural Network |
| title_short | Identification of Tool-Wear State Using Information Fusion and SSA–BP Neural Network |
| title_sort | identification of tool wear state using information fusion and ssa bp neural network |
| topic | tool-wear-state identification information fusion SSA–BP neural network PCA feature extraction |
| url | https://www.mdpi.com/2075-1702/13/4/256 |
| work_keys_str_mv | AT zishuowang identificationoftoolwearstateusinginformationfusionandssabpneuralnetwork AT hongweicui identificationoftoolwearstateusinginformationfusionandssabpneuralnetwork AT shuningliang identificationoftoolwearstateusinginformationfusionandssabpneuralnetwork AT taoding identificationoftoolwearstateusinginformationfusionandssabpneuralnetwork AT xingquangao identificationoftoolwearstateusinginformationfusionandssabpneuralnetwork |