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Variability-enhanced knowledge-based engineering (VEN) for reconfigurable molds

Author

Listed:
  • Zeeshan Qaiser

    (College of Civil Engineering, Tongji University)

  • Kunlin Yang

    (University of Michigan and Shanghai Jiao Tong University Joint Institute)

  • Rui Chen

    (University of Michigan and Shanghai Jiao Tong University Joint Institute)

  • Shane Johnson

    (University of Michigan and Shanghai Jiao Tong University Joint Institute
    Shanghai Jiao Tong University)

Abstract

Mass production of high geometric variability surfaces, particularly in customized medical or ergonomic systems inherently display regions characterized by large variations in size, shape, and the spatial distribution. These high variability requirements result in low scalability, low production capacity, high complexity, and high maintenance and operational costs of manufacturing systems. Manufacturing molds need to physically emulate normal shapes with large variation while maintaining low complexity. A surface mold actuated with reconfigurable tooling (SMART) is proposed for molds with high variability capacity requirements for Custom Foot Orthoses (CFOs). The proposed Variability Enhanced-KBE (VEN) solution integrates a knowledge base of variations using statistical shape modeling (SSM), development of a parametric finite element (FE) model, a stepwise design optimization, and Machine Learning (ML) control. The experimentally validated FE model of the SMART system (RMSE

Suggested Citation

  • Zeeshan Qaiser & Kunlin Yang & Rui Chen & Shane Johnson, 2025. "Variability-enhanced knowledge-based engineering (VEN) for reconfigurable molds," Journal of Intelligent Manufacturing, Springer, vol. 36(5), pages 3097-3109, June.
    • Handle: RePEc:spr:joinma:v:36:y:2025:i:5:d:10.1007_s10845-024-02361-y
    DOI: 10.1007/s10845-024-02361-y
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    References listed on IDEAS

    as
    1. Hao Sun & Shengqiang Zhao & Fangyu Peng & Rong Yan & Lin Zhou & Teng Zhang & Chi Zhang, 2024. "In-situ prediction of machining errors of thin-walled parts: an engineering knowledge based sparse Bayesian learning approach," Journal of Intelligent Manufacturing, Springer, vol. 35(1), pages 387-411, January.
    2. Farzam Farbiz & Mohd Salahuddin Habibullah & Brahim Hamadicharef & Tomasz Maszczyk & Saurabh Aggarwal, 2023. "Knowledge-embedded machine learning and its applications in smart manufacturing," Journal of Intelligent Manufacturing, Springer, vol. 34(7), pages 2889-2906, October.
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