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

Zeeshan Qaiser, Kunlin Yang, Rui Chen and Shane Johnson ()
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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

Journal of Intelligent Manufacturing, 2025, vol. 36, issue 5, No 8, 3097-3109

Abstract: 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

Keywords: Knowledge-based engineering; Optimization; Machine learning; Variation modeling; Reconfigurable molds (search for similar items in EconPapers)
Date: 2025
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DOI: 10.1007/s10845-024-02361-y

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