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A novel tolerance geometric method based on machine learning

Author

Listed:
  • Lu-jun Cui

    (Zhongyuan University of Technology)

  • Man-ying Sun

    (Zhongyuan University of Technology)

  • Yan-long Cao

    (Zhongyuan University of Technology)

  • Qi-jian Zhao

    (China Academy of Engineering Physics)

  • Wen-han Zeng

    (Zhongyuan University of Technology)

  • Shi-rui Guo

    (Zhongyuan University of Technology)

Abstract

In most cases, designers must manually specify geometric tolerance types and values when designing mechanical products. For the same nominal geometry, different designers may specify different types and values of geometric tolerances. To reduce the uncertainty and realize the tolerance specification automatically, a tolerance specification method based on machine learning is proposed. The innovation of this paper is to find out the information that affects geometric tolerances selection and use machine learning methods to generate tolerance specifications. The realization of tolerance specifications is changed from rule-driven to data-driven. In this paper, feature engineering is performed on the data for the application scenarios of tolerance specifications, which improves the performance of the machine learning model. This approach firstly considers the past tolerance specification schemes as cases and sets up the cases to the tolerance specification database which contains information such as datum reference frame, positional relationship, spatial relationship, and product cost. Then perform feature engineering on the data and established machine learning algorithm to convert the tolerance specification problem into an optimization problem. Finally, a gear reducer as a case study is given to verify the method. The results are evaluated with three different machine learning evaluation indicators and made a comparison with the tolerance specification method in the industry. The final results show that the machine learning algorithm can automatically generate tolerance specifications, and after feature engineering, the accuracy of the tolerance specification results is improved.

Suggested Citation

  • Lu-jun Cui & Man-ying Sun & Yan-long Cao & Qi-jian Zhao & Wen-han Zeng & Shi-rui Guo, 2021. "A novel tolerance geometric method based on machine learning," Journal of Intelligent Manufacturing, Springer, vol. 32(3), pages 799-821, March.
    • Handle: RePEc:spr:joinma:v:32:y:2021:i:3:d:10.1007_s10845-020-01706-7
    DOI: 10.1007/s10845-020-01706-7
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    References listed on IDEAS

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    1. Tian Wang & Meina Qiao & Mengyi Zhang & Yi Yang & Hichem Snoussi, 2020. "Data-driven prognostic method based on self-supervised learning approaches for fault detection," Journal of Intelligent Manufacturing, Springer, vol. 31(7), pages 1611-1619, October.
    2. Atul Mishra & Sankha Deb, 2019. "Assembly sequence optimization using a flower pollination algorithm-based approach," Journal of Intelligent Manufacturing, Springer, vol. 30(2), pages 461-482, February.
    3. Yueyi Zhang & Lixiang Li & Mingshun Song & Ronghua Yi, 2019. "Optimal tolerance design of hierarchical products based on quality loss function," Journal of Intelligent Manufacturing, Springer, vol. 30(1), pages 185-192, January.
    4. Germán González Rodríguez & Jose M. Gonzalez-Cava & Juan Albino Méndez Pérez, 2020. "An intelligent decision support system for production planning based on machine learning," Journal of Intelligent Manufacturing, Springer, vol. 31(5), pages 1257-1273, June.
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