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

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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 < 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.

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Data Availibility Statement

Not Applicable.

Abbreviations

IFS:

Interfacial systems

DMS:

Dedicated manufacturing system

GMS:

Generalized manufacturing system

FMS:

Flexible manufacturing system

RM:

Reconfigurable mold

SMART:

Surface mold actuated with reconfigurable tooling

CFO:

Custom foot orthoses

SSM:

Statistical shape modeling

PCA:

Principal component analysis

MLA:

Medial longitudinal arch

KBE:

Knowledge-based engineering

VEN:

Variability Enhanced Knowledge-based engineering

BC:

Boundary condition

RMSE:

Root mean square error

RS:

Representative shape

\(\mu _i\) :

Mean value of the scores

M, N :

Coefficient matrices used for the first and second mode

X :

Horizontal location of the actuators

Y :

Vertical location of the actuators

\(\sigma _i\) :

Standard deviation scores

U :

Translations

\(U_R\) :

Rotations

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Funding

This research is funded by the National Nature Science Foundation of China under grant numbers 52050410329 and 517500410692, and the Joint research fund for interdisciplinary research, Tongji University under grant number 2023-2-YB-16.

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Authors and Affiliations

  1. Department of Disaster Mitigation for Structures (DDMS), College of Civil Engineering, Tongji University, Shanghai, 200092, Shanghai, China

    Zeeshan Qaiser

  2. University of Michigan and Shanghai Jiao Tong University Joint Institute, Shanghai, 200240, Shanghai, China

    Kunlin Yang, Rui Chen & Shane Johnson

  3. State Key Laboratory of Mechanical Systems and Vibration, Shanghai Jiao Tong University, 800 Dongchuan Rd, Shanghai, 200240, China

    Shane Johnson

Authors
  1. Zeeshan Qaiser
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  2. Kunlin Yang
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  3. Rui Chen
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  4. Shane Johnson
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Contributions

Zeeshan Qaiser, Kunlin Yang, Shane Johnson worked on conceptualization, methodology, and preparation of the original draft with input from all authors. Both Kunlin Yang and Rui Chen developed the prototype and conducted the experiments. Dr. Shane Johnson supervised the project.

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Correspondence to Shane Johnson.

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Qaiser, Z., Yang, K., Chen, R. et al. Variability-enhanced knowledge-based engineering (VEN) for reconfigurable molds. J Intell Manuf (2024). https://doi.org/10.1007/s10845-024-02361-y

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