A Nonintrusive Nonlinear Model Reduction Method for Thermal Cycling-Induced Viscoplastic Deformation Problems Based on Segmented Gaussian Process Regression Machine Learning
Numerical simulation of thermal cycling-induced viscoplastic deformation is important to design a reliable underfilled flip chip package, where the time histories of viscoplastic strains are applied to predict the fatigue life of solder joints. However, the high-fidelity full model simulations of a...
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| Format: | Article |
| Language: | English |
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IEEE
2025-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/10824784/ |
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| author | Jiunn-Horng Lee Chia-Hsiu Ou Chih-Min Yao Ming-Hsiao Lee |
| author_facet | Jiunn-Horng Lee Chia-Hsiu Ou Chih-Min Yao Ming-Hsiao Lee |
| author_sort | Jiunn-Horng Lee |
| collection | DOAJ |
| description | Numerical simulation of thermal cycling-induced viscoplastic deformation is important to design a reliable underfilled flip chip package, where the time histories of viscoplastic strains are applied to predict the fatigue life of solder joints. However, the high-fidelity full model simulations of a flip chip package under thermal cycling are time intensive tasks. Nonintrusive model order reduction combined with machine learning is increasingly being used to create efficient surrogate models for problems with high computational costs. In this paper, a segmented Gaussian process regression machine learning method combined with proper orthogonal decomposition is proposed to build the reduced order models of underfilled flip chip packages for thermal cycling-induced viscoplastic displacements and strains, respectively. Simulation results show the derived reduced order models can effectively predict the viscoplastic displacements and strains induced by thermal cycling across the entire solution domain and time history for different material properties of underfills and provide better accuracy than those generated by the non-segmented Gaussian process regression method. |
| format | Article |
| id | doaj-art-a85e3e043dd5418d864188119f4d91c7 |
| institution | Kabale University |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-a85e3e043dd5418d864188119f4d91c72025-01-14T00:02:43ZengIEEEIEEE Access2169-35362025-01-01134326434010.1109/ACCESS.2025.352585110824784A Nonintrusive Nonlinear Model Reduction Method for Thermal Cycling-Induced Viscoplastic Deformation Problems Based on Segmented Gaussian Process Regression Machine LearningJiunn-Horng Lee0https://orcid.org/0000-0002-7059-2393Chia-Hsiu Ou1Chih-Min Yao2Ming-Hsiao Lee3National Center for High-performance Computing, National Applied Research Laboratories, Hsinchu, TaiwanDepartment of Electrical Engineering, National Taiwan University, Taipei City, TaiwanNational Center for High-performance Computing, National Applied Research Laboratories, Hsinchu, TaiwanNational Center for High-performance Computing, National Applied Research Laboratories, Hsinchu, TaiwanNumerical simulation of thermal cycling-induced viscoplastic deformation is important to design a reliable underfilled flip chip package, where the time histories of viscoplastic strains are applied to predict the fatigue life of solder joints. However, the high-fidelity full model simulations of a flip chip package under thermal cycling are time intensive tasks. Nonintrusive model order reduction combined with machine learning is increasingly being used to create efficient surrogate models for problems with high computational costs. In this paper, a segmented Gaussian process regression machine learning method combined with proper orthogonal decomposition is proposed to build the reduced order models of underfilled flip chip packages for thermal cycling-induced viscoplastic displacements and strains, respectively. Simulation results show the derived reduced order models can effectively predict the viscoplastic displacements and strains induced by thermal cycling across the entire solution domain and time history for different material properties of underfills and provide better accuracy than those generated by the non-segmented Gaussian process regression method.https://ieeexplore.ieee.org/document/10824784/Flip chip packageGaussian process regressionmachine learningproper orthogonal decompositionreduced order modelviscoplastic |
| spellingShingle | Jiunn-Horng Lee Chia-Hsiu Ou Chih-Min Yao Ming-Hsiao Lee A Nonintrusive Nonlinear Model Reduction Method for Thermal Cycling-Induced Viscoplastic Deformation Problems Based on Segmented Gaussian Process Regression Machine Learning IEEE Access Flip chip package Gaussian process regression machine learning proper orthogonal decomposition reduced order model viscoplastic |
| title | A Nonintrusive Nonlinear Model Reduction Method for Thermal Cycling-Induced Viscoplastic Deformation Problems Based on Segmented Gaussian Process Regression Machine Learning |
| title_full | A Nonintrusive Nonlinear Model Reduction Method for Thermal Cycling-Induced Viscoplastic Deformation Problems Based on Segmented Gaussian Process Regression Machine Learning |
| title_fullStr | A Nonintrusive Nonlinear Model Reduction Method for Thermal Cycling-Induced Viscoplastic Deformation Problems Based on Segmented Gaussian Process Regression Machine Learning |
| title_full_unstemmed | A Nonintrusive Nonlinear Model Reduction Method for Thermal Cycling-Induced Viscoplastic Deformation Problems Based on Segmented Gaussian Process Regression Machine Learning |
| title_short | A Nonintrusive Nonlinear Model Reduction Method for Thermal Cycling-Induced Viscoplastic Deformation Problems Based on Segmented Gaussian Process Regression Machine Learning |
| title_sort | nonintrusive nonlinear model reduction method for thermal cycling induced viscoplastic deformation problems based on segmented gaussian process regression machine learning |
| topic | Flip chip package Gaussian process regression machine learning proper orthogonal decomposition reduced order model viscoplastic |
| url | https://ieeexplore.ieee.org/document/10824784/ |
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