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Development of surrogate models of clamp configuration for optical glass lens centering through finite element analysis and machine learning


In this study, the clamping stress and force involved in the centering of optical glass lens were evaluated and quantified. On the basis of the key design parameters of the examined clamps, the finite element method was applied to predict clamping stress under various parameter combinations. Support vector regression, Gaussian process regression, and adaptive neuro fuzzy inference system algorithm of surrogate models were established using the results obtained through finite element simulation. These surrogate models, which can predict clamping stress on the basis of key parameters, can reduce the time required to perform finite element analysis while providing references for optimizing clamp configuration.

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The authors are grateful for the support of the Research Project of the Ministry of Science and Technology, Taiwan (MOST 110–2222-E-007 -009 -MY3 & MOST 110–2218-E-007 -053).

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Authors and Affiliations



Chun-Wei Liu: Conceptualization, supervision Shiau-Cheng Shiu: Finite element simulation, convergence analysis Kai-Hung Yu: Experiment design, writing, algorithmic modeling.

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Correspondence to Chun-Wei Liu.

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Yu, KH., Shiu, SC. & Liu, CW. Development of surrogate models of clamp configuration for optical glass lens centering through finite element analysis and machine learning. Int J Adv Manuf Technol 121, 8209–8220 (2022).

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  • Centering process
  • Surrogate model
  • Finite element analysis
  • Machine learning