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Research on the Optimization Model of the Abrasive Blocks Using Weighted Case-Based Reasoning

Author

Listed:
  • Wei Gao
  • Shengqiang Yang
  • Jianyan Tian
  • Yan Yang
  • Xiaojian Fan
  • Wanzhen Wei

Abstract

Barrel finishing process is a universal method to improve the surface quality of parts. It is widely used in high-performance parts of high-end equipment. As a necessary tool consumable for barrel finishing process, the characteristic parameters of the abrasive blocks affect the processing quality and production efficiency. However, the current method for selecting the abrasive blocks requires large number of experiments based on the operator’s extensive experience, which does not meet the rapid development needs of the barrel finishing process. Therefore, this paper proposes a case-based reasoning model with variable weights to achieve the intelligent optimization of the abrasive blocks. Based on the in-depth analysis of the characteristics of the barrel finishing process, a reasonable case base is established firstly, which is to determine the comprehensive case features and the solution of the case. AHP (analytic hierarchy process) is proposed to determine the weight of case features and to dynamically adjust the weight of case features according to the characteristics of the parts to be processed and users’ processing requirements. The results show that the proposed case-based reasoning model with variable weights can quickly, accurately, and reasonably select the abrasive blocks during the process of making processing technique of the barrel finishing, which will lay a necessary foundation for the effective implementation of the barrel finishing process and contribute significantly to the improvement of its efficiency.

Suggested Citation

  • Wei Gao & Shengqiang Yang & Jianyan Tian & Yan Yang & Xiaojian Fan & Wanzhen Wei, 2019. "Research on the Optimization Model of the Abrasive Blocks Using Weighted Case-Based Reasoning," Mathematical Problems in Engineering, Hindawi, vol. 2019, pages 1-12, December.
  • Handle: RePEc:hin:jnlmpe:8981231
    DOI: 10.1155/2019/8981231
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