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Variability-enhanced knowledge-based engineering (VEN) for reconfigurable molds
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DOI：10.1007/s10845-024-02361-y.png)
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摘要
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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 < 0.5mm) is used for design optimization and dataset generation for the ML control algorithm. The fabricated SMART system employs discrete coarse and fine size/shape adjustment in low and high variation areas respectively. The SMART system's experimental validation confirms an accuracy range of 0.3-0.5mm (RMSE) across the population, showing a 84% improvement over the benchmark. This VEN SMART approach may improve manufacturing in various high variability freeform surface applications.
Keyword:
Knowledge-based engineering
Optimization
Machine learning
Variation modeling
Reconfigurable molds
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3.5K
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1.1W
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