Classification-Based Parameter Optimization Approach of the Turning Process
The turning process is a widely used machining process, and its productivity has a significant impact on the cost and profit in industrial enterprises. Currently, it is difficult to effectively determine the optimum process parameters under complex conditions. To address this issue, a classification...
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| Format: | Article |
| Language: | English |
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MDPI AG
2024-11-01
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| Series: | Machines |
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| Online Access: | https://www.mdpi.com/2075-1702/12/11/805 |
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| author | Lei Yang Yibo Jiang Yawei Yang Guowen Zeng Zongzhi Zhu Jiaxi Chen |
| author_facet | Lei Yang Yibo Jiang Yawei Yang Guowen Zeng Zongzhi Zhu Jiaxi Chen |
| author_sort | Lei Yang |
| collection | DOAJ |
| description | The turning process is a widely used machining process, and its productivity has a significant impact on the cost and profit in industrial enterprises. Currently, it is difficult to effectively determine the optimum process parameters under complex conditions. To address this issue, a classification-based parameter optimization approach of the turning process is proposed in this paper, which aims to provide feasible optimization suggestions of process parameters and consists of a classification model and several optimization strategies. Specifically, the classification model is used to separate the whole complex process into different substages to reduce difficulties of the further optimization, and it achieves high accuracy and strong anti-interference in the identification of substages by integrating the advantages of an encoder-decoder framework, attention mechanism, and major voting. Additionally, during the optimization process of each substage, Dynamic Time Warping (DTW) and K-Nearest Neighbor (KNN) are utilized to eliminate the negative impact of cutting tool wear status on optimization results at first. Then, the envelope curve strategy and boxplot method succeed in the adaptive calculation of a parameter threshold and the detection of optimizable items. According to these optimization strategies, the proposed approach performs well in the provision of effective optimization suggestions. Ultimately, the proposed approach is verified by a bearing production line. Experimental results demonstrate that the proposed approach achieves a significant productivity improvement of 23.43% in the studied production line. |
| format | Article |
| id | doaj-art-97480b74aa374f6898cd7cf84c92402b |
| institution | DOAJ |
| issn | 2075-1702 |
| language | English |
| publishDate | 2024-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Machines |
| spelling | doaj-art-97480b74aa374f6898cd7cf84c92402b2025-08-20T02:48:05ZengMDPI AGMachines2075-17022024-11-01121180510.3390/machines12110805Classification-Based Parameter Optimization Approach of the Turning ProcessLei Yang0Yibo Jiang1Yawei Yang2Guowen Zeng3Zongzhi Zhu4Jiaxi Chen5ZJU-Hangzhou Global Scientific and Technological Innovation Center, Hangzhou 311215, ChinaThe Jiaxing Shutuo Technology Co., Ltd., Jiaxing 314031, ChinaChina Unicom Jinhua Branch, Jinhua 321013, ChinaChina Unicom Jinhua Branch, Jinhua 321013, ChinaThe Zhejiang Guoli Security Technology Co., Ltd., Hangzhou 310059, ChinaFaculty of Science and Engineering, University of Nottingham, Ningbo 315000, ChinaThe turning process is a widely used machining process, and its productivity has a significant impact on the cost and profit in industrial enterprises. Currently, it is difficult to effectively determine the optimum process parameters under complex conditions. To address this issue, a classification-based parameter optimization approach of the turning process is proposed in this paper, which aims to provide feasible optimization suggestions of process parameters and consists of a classification model and several optimization strategies. Specifically, the classification model is used to separate the whole complex process into different substages to reduce difficulties of the further optimization, and it achieves high accuracy and strong anti-interference in the identification of substages by integrating the advantages of an encoder-decoder framework, attention mechanism, and major voting. Additionally, during the optimization process of each substage, Dynamic Time Warping (DTW) and K-Nearest Neighbor (KNN) are utilized to eliminate the negative impact of cutting tool wear status on optimization results at first. Then, the envelope curve strategy and boxplot method succeed in the adaptive calculation of a parameter threshold and the detection of optimizable items. According to these optimization strategies, the proposed approach performs well in the provision of effective optimization suggestions. Ultimately, the proposed approach is verified by a bearing production line. Experimental results demonstrate that the proposed approach achieves a significant productivity improvement of 23.43% in the studied production line.https://www.mdpi.com/2075-1702/12/11/805classificationprocess optimizationturning processlong short-term memoryattention mechanismcutting tool |
| spellingShingle | Lei Yang Yibo Jiang Yawei Yang Guowen Zeng Zongzhi Zhu Jiaxi Chen Classification-Based Parameter Optimization Approach of the Turning Process Machines classification process optimization turning process long short-term memory attention mechanism cutting tool |
| title | Classification-Based Parameter Optimization Approach of the Turning Process |
| title_full | Classification-Based Parameter Optimization Approach of the Turning Process |
| title_fullStr | Classification-Based Parameter Optimization Approach of the Turning Process |
| title_full_unstemmed | Classification-Based Parameter Optimization Approach of the Turning Process |
| title_short | Classification-Based Parameter Optimization Approach of the Turning Process |
| title_sort | classification based parameter optimization approach of the turning process |
| topic | classification process optimization turning process long short-term memory attention mechanism cutting tool |
| url | https://www.mdpi.com/2075-1702/12/11/805 |
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