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Journal of Graphics ›› 2024, Vol. 45 ›› Issue (2): 399-408.DOI: 10.11996/JG.j.2095-302X.2024020399

• Digital Design and Manufacture Special • Previous Articles     Next Articles

Tacit process knowledge acquisition methods for the parts machining

ZHANG Yiming1(), LIU Jinfeng1(), CHEN Yajie2, QU Pengfei1, JING Xuwen1, LIU Xiaojun3   

  1. 1. School of Mechanical Engineering, Jiangsu University of Science and Technology, Zhenjiang Jiangsu 212028, China
    2. Shanghai Marine Equipment Research Institute, Shanghai 200030, China
    3. School of Mechanical Engineering, Southeast University, Nanjing Jiangsu 211100, China
  • Received:2023-09-06 Revised:2023-12-24 Online:2024-04-30 Published:2024-04-30
  • Contact: LIU Jinfeng (1987-), associate professor, Ph.D. His main research interests cover digitial design and manufacturing, etc. E-mail:liujinfeng@just.edu.cn
  • About author:ZHANG Yiming (2000-), master student. His main research interests cover digitial design and manufacturing, etc. E-mail:908891371@qq.com
  • Supported by:
    National Natural Science Foundation of China(52075229);National Natural Science Foundation of China(52371324);The Natural Science Foundation of the Jiangsu Higher Education Institutions of China(20KJA4600009)

Abstract:

With the widespread application of digital processes in the manufacturing industry, how to efficiently utilize the accumulated process knowledge has become the key to enhancing the efficiency and quality of process design. However, there are technical bottlenecks in acquiring, describing, and transforming tacit process knowledge, hindering the adoption of the intelligent process design mode. Therefore, a method of acquiring tacit process knowledge for processing complex parts was proposed. Firstly, the equal-width method was employed to discretize the structured process data, a text mining based tacit process knowledge acquisition process was constructed, and tacit process knowledge was expressed through production rules. Then, a knowledge reasoning method was proposed, which combined case-based reasoning and rule-based reasoning. The recognition of tacit process knowledge was achieved using the nearest neighbor algorithm. Finally, the method for acquiring processing tacit process knowledge was effectively validated using complex machining parts of marine diesel engine cylinder heads as the verification object.

Key words: tacit knowledge, production rule, text mining, case-based reasoning, rule-based reasoning, nearest neighbor algorithm

CLC Number: