Exploration of Cutting Processing Mode of Low-Rigidity Parts for Intelligent Manufacturing
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| 出版年: | Micromachines vol. 16, no. 6 (2025), p. 624-646 |
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| 第一著者: | |
| その他の著者: | , , , |
| 出版事項: |
MDPI AG
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| 主題: | |
| オンライン･アクセス: | Citation/Abstract Full Text + Graphics Full Text - PDF |
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| 045 | 2 | |b d20250101 |b d20251231 | |
| 084 | |a 231537 |2 nlm | ||
| 100 | 1 | |a Zhu, Jianping |u Rongcheng College, Harbin University of Science and Technology, Weihai 264800, China; zhujianping2017@126.com (J.Z.); woshipenghui@126.com (H.P.) | |
| 245 | 1 | |a Exploration of Cutting Processing Mode of Low-Rigidity Parts for Intelligent Manufacturing | |
| 260 | |b MDPI AG |c 2025 | ||
| 513 | |a Journal Article | ||
| 520 | 3 | |a With the development of intelligent manufacturing technology, the manufacturing industry is gradually realizing intelligent production. Especially for metal cutting with extremely complex processes, it is of great significance to realize intelligence. Taking the cutting process of aero-engine typical low-rigidity parts as the main line, this article builds an intelligent processing architecture based on a big data platform, which includes customized design of cutting tools, intelligent optimization of cutting parameters, simulation of cutting conditions, and online monitoring and control of cutting processes. At the same time, the realization of related key technologies is explained. Then, this article introduces in detail the intelligent decision-making process based on deep learning, the customized tool design process based on structural features, the simulation process of cutting based on geometric features of parts, as well as the monitoring and control process of Numerical Control (NC) machining based on condition perception. In addition, based on the processing requirements and difficulties of specific parts, formulate a specific intelligent implementation plan under this processing mode. Through the implementation of the above architecture and key technologies, the cutting processing system can automatically optimize the cutting parameters according to real-time working conditions and adjust its own cutting conditions. At the same time, machine tool condition, cutting tool condition, and low-rigidity part condition are real-time monitored to achieve high-precision, efficient, intelligent, and precise cutting of low-rigidity parts. The proposed architecture can provide a reference model for the research and application of intelligent cutting technology for low-rigidity parts. | |
| 651 | 4 | |a China | |
| 653 | |a Cutting tools | ||
| 653 | |a Big Data | ||
| 653 | |a Accuracy | ||
| 653 | |a Deep learning | ||
| 653 | |a Machinery condition monitoring | ||
| 653 | |a Artificial intelligence | ||
| 653 | |a Numerical controls | ||
| 653 | |a Metal cutting | ||
| 653 | |a Machining | ||
| 653 | |a Optimization | ||
| 653 | |a Machine tools | ||
| 653 | |a Cutting parameters | ||
| 653 | |a Manufacturing | ||
| 653 | |a Rigidity | ||
| 653 | |a Deformation | ||
| 653 | |a Real time | ||
| 653 | |a Cloud computing | ||
| 653 | |a Information technology | ||
| 653 | |a Customization | ||
| 653 | |a Intelligent manufacturing systems | ||
| 653 | |a Internet of Things | ||
| 653 | |a Efficiency | ||
| 653 | |a Bottlenecks | ||
| 700 | 1 | |a Liu Xinna |u Rongcheng College, Harbin University of Science and Technology, Weihai 264800, China; zhujianping2017@126.com (J.Z.); woshipenghui@126.com (H.P.) | |
| 700 | 1 | |a Peng, Hui |u Rongcheng College, Harbin University of Science and Technology, Weihai 264800, China; zhujianping2017@126.com (J.Z.); woshipenghui@126.com (H.P.) | |
| 700 | 1 | |a Liu, Wei |u Rongcheng Kangyi New Material Technology Co., Ltd., Weihai 264800, China | |
| 700 | 1 | |a Li, Zhiyong |u Mechanical Engineering College, Shandong University of Technology, Zibo 255049, China; lzy761012@sdut.edu.cn | |
| 773 | 0 | |t Micromachines |g vol. 16, no. 6 (2025), p. 624-646 | |
| 786 | 0 | |d ProQuest |t Engineering Database | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/3223926622/abstract/embedded/7BTGNMKEMPT1V9Z2?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text + Graphics |u https://www.proquest.com/docview/3223926622/fulltextwithgraphics/embedded/7BTGNMKEMPT1V9Z2?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text - PDF |u https://www.proquest.com/docview/3223926622/fulltextPDF/embedded/7BTGNMKEMPT1V9Z2?source=fedsrch |