Calibration of viscoplastic model for modified polyphenylene oxide polymer using approximate optimization technique

Su Haeng Hur, Jaehyeok Doh, Shin Ill Kang, Joon Sang Lee, Jongsoo Lee

Research output: Contribution to journalArticle

Abstract

In this study, mechanical properties of the modified polyphenylene oxide (MPPO) were measured using a uniaxial tensile test. Based on this, the modified two-layer viscoplastic model describing the mechanical behavior of MPPO was applied to conduct a parameter study concerning material constants through finite element analysis. Through this, meaningful material constants were determined for the material model, using which, a computational design of experiments using the interior central composite design (ICCD) was established. Then, the root mean square error (RMSE) for load and displacement between test and analysis was calculated. Subsequently, surrogate models for the relationship between material constants (input) and RMSE (output) were generated using a back-propagation neural network (BPN). The optimization problem of material constants was formulated and using the non-dominant sorting genetic algorithm-II (NSGA-II), an optimal solution of material constants was calculated. Based on these results, finite element analysis was performed to validate the model describing the mechanical behavior of the MPPO material by quantitatively comparing the results obtained using the conventional and modified two-layer viscoplastic model with experiment data.

Original languageEnglish
Pages (from-to)871-879
Number of pages9
JournalTransactions of the Korean Society of Mechanical Engineers, A
Volume42
Issue number10
DOIs
Publication statusPublished - 2018 Oct 1

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Polyphenylene oxides
Calibration
Polymers
Mean square error
Finite element method
Backpropagation
Sorting
Design of experiments
Genetic algorithms
Neural networks
Mechanical properties
Composite materials

All Science Journal Classification (ASJC) codes

  • Mechanical Engineering

Cite this

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title = "Calibration of viscoplastic model for modified polyphenylene oxide polymer using approximate optimization technique",
abstract = "In this study, mechanical properties of the modified polyphenylene oxide (MPPO) were measured using a uniaxial tensile test. Based on this, the modified two-layer viscoplastic model describing the mechanical behavior of MPPO was applied to conduct a parameter study concerning material constants through finite element analysis. Through this, meaningful material constants were determined for the material model, using which, a computational design of experiments using the interior central composite design (ICCD) was established. Then, the root mean square error (RMSE) for load and displacement between test and analysis was calculated. Subsequently, surrogate models for the relationship between material constants (input) and RMSE (output) were generated using a back-propagation neural network (BPN). The optimization problem of material constants was formulated and using the non-dominant sorting genetic algorithm-II (NSGA-II), an optimal solution of material constants was calculated. Based on these results, finite element analysis was performed to validate the model describing the mechanical behavior of the MPPO material by quantitatively comparing the results obtained using the conventional and modified two-layer viscoplastic model with experiment data.",
author = "Hur, {Su Haeng} and Jaehyeok Doh and Kang, {Shin Ill} and Lee, {Joon Sang} and Jongsoo Lee",
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AU - Hur, Su Haeng

AU - Doh, Jaehyeok

AU - Kang, Shin Ill

AU - Lee, Joon Sang

AU - Lee, Jongsoo

PY - 2018/10/1

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N2 - In this study, mechanical properties of the modified polyphenylene oxide (MPPO) were measured using a uniaxial tensile test. Based on this, the modified two-layer viscoplastic model describing the mechanical behavior of MPPO was applied to conduct a parameter study concerning material constants through finite element analysis. Through this, meaningful material constants were determined for the material model, using which, a computational design of experiments using the interior central composite design (ICCD) was established. Then, the root mean square error (RMSE) for load and displacement between test and analysis was calculated. Subsequently, surrogate models for the relationship between material constants (input) and RMSE (output) were generated using a back-propagation neural network (BPN). The optimization problem of material constants was formulated and using the non-dominant sorting genetic algorithm-II (NSGA-II), an optimal solution of material constants was calculated. Based on these results, finite element analysis was performed to validate the model describing the mechanical behavior of the MPPO material by quantitatively comparing the results obtained using the conventional and modified two-layer viscoplastic model with experiment data.

AB - In this study, mechanical properties of the modified polyphenylene oxide (MPPO) were measured using a uniaxial tensile test. Based on this, the modified two-layer viscoplastic model describing the mechanical behavior of MPPO was applied to conduct a parameter study concerning material constants through finite element analysis. Through this, meaningful material constants were determined for the material model, using which, a computational design of experiments using the interior central composite design (ICCD) was established. Then, the root mean square error (RMSE) for load and displacement between test and analysis was calculated. Subsequently, surrogate models for the relationship between material constants (input) and RMSE (output) were generated using a back-propagation neural network (BPN). The optimization problem of material constants was formulated and using the non-dominant sorting genetic algorithm-II (NSGA-II), an optimal solution of material constants was calculated. Based on these results, finite element analysis was performed to validate the model describing the mechanical behavior of the MPPO material by quantitatively comparing the results obtained using the conventional and modified two-layer viscoplastic model with experiment data.

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