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Decision rule mining for machining method chains based on rough set theory

Author

Listed:
  • Rui Wang

    (Harbin Institute of Technology)

  • Xiangyu Guo

    (Harbin Institute of Technology)

  • Shisheng Zhong

    (Harbin Institute of Technology)

  • Gaolei Peng

    (Harbin Institute of Technology)

  • Lin Wang

    (Harbin Institute of Technology)

Abstract

Decision rules for machining method chains mined from historical machining documents can help technologists quickly design new machining method chains. However, the main factor that limits the practical application of existing rough set models is that the boundary regions are too large. Therefore, a decomposition-reorganization method (DRM) is proposed to mine rules for machining method chains. First, binary coding is used to decompose the existing machining method chains, and the decision rules for a single machining method are mined based on rough set reduction. Then, machining method chains are obtained by reorganizing the machining methods in accordance with the decision rules. DRM can eliminate the boundary regions without human intervention and recommend machining method chains for all features whose parameters have appeared in historical machining documents. Finally, three types of shell parts are used to verify the effectiveness of DRM.

Suggested Citation

  • Rui Wang & Xiangyu Guo & Shisheng Zhong & Gaolei Peng & Lin Wang, 2022. "Decision rule mining for machining method chains based on rough set theory," Journal of Intelligent Manufacturing, Springer, vol. 33(3), pages 799-807, March.
    • Handle: RePEc:spr:joinma:v:33:y:2022:i:3:d:10.1007_s10845-020-01692-w
    DOI: 10.1007/s10845-020-01692-w
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    References listed on IDEAS

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    1. Peter Chhim & Ratna Babu Chinnam & Noureddin Sadawi, 2019. "Product design and manufacturing process based ontology for manufacturing knowledge reuse," Journal of Intelligent Manufacturing, Springer, vol. 30(2), pages 905-916, February.
    2. Yingxin Ye & Tianliang Hu & Yan Yang & Wendan Zhu & Chengrui Zhang, 2020. "A knowledge based intelligent process planning method for controller of computer numerical control machine tools," Journal of Intelligent Manufacturing, Springer, vol. 31(7), pages 1751-1767, October.
    3. Zhigang Jiang & Ya Jiang & Yan Wang & Hua Zhang & Huajun Cao & Guangdong Tian, 2019. "A hybrid approach of rough set and case-based reasoning to remanufacturing process planning," Journal of Intelligent Manufacturing, Springer, vol. 30(1), pages 19-32, January.
    4. Zaifang Zhang & Danhua Xu & Egon Ostrosi & Li Yu & Beibei Fan, 2019. "A systematic decision-making method for evaluating design alternatives of product service system based on variable precision rough set," Journal of Intelligent Manufacturing, Springer, vol. 30(4), pages 1895-1909, April.
    5. Ercan Oztemel & Samet Gursev, 2020. "Literature review of Industry 4.0 and related technologies," Journal of Intelligent Manufacturing, Springer, vol. 31(1), pages 127-182, January.
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    Cited by:

    1. Chen-Fu Chien & Hsin-Jung Wu, 2024. "Integrated circuit probe card troubleshooting based on rough set theory for advanced quality control and an empirical study," Journal of Intelligent Manufacturing, Springer, vol. 35(1), pages 275-287, January.

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