Calibration and surrogate model-based sensitivity analysis of crystal plasticity finite element models
Crystal plasticity models are powerful tools for predicting the deformation behaviour of polycrystalline materials accounting for underlying grain morphology and texture. These models typically have a large number of parameters, an understanding of which is required to effectively calibrate and appl...
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| Format: | Article |
| Language: | English |
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Elsevier
2024-11-01
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| Series: | Materials & Design |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S0264127524007846 |
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| _version_ | 1850137738651631616 |
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| author | Hugh Dorward David M. Knowles Eralp Demir Mahmoud Mostafavi Matthew J. Peel |
| author_facet | Hugh Dorward David M. Knowles Eralp Demir Mahmoud Mostafavi Matthew J. Peel |
| author_sort | Hugh Dorward |
| collection | DOAJ |
| description | Crystal plasticity models are powerful tools for predicting the deformation behaviour of polycrystalline materials accounting for underlying grain morphology and texture. These models typically have a large number of parameters, an understanding of which is required to effectively calibrate and apply the model. This study presents a structured framework for the global sensitivity analysis of the effect of crystal plasticity parameters on model outputs. Due to the computational cost of evaluating crystal plasticity models multiple times within a finite element framework, a Gaussian process regression surrogate was constructed and used to conduct the sensitivity analysis. Influential parameters from the sensitivity analysis were carried forward for calibration using both a local Nelder-Mead and global differential evolution optimisation algorithm. The results show that the surrogate based global sensitivity analysis is able to efficiently identify influential crystal plasticity parameters and parameter combinations. Comparison of the Nelder-Mead and differential evolution algorithms demonstrated that only the differential evolution algorithm was able to reliably find the global optimum due to the presence of local minima in the calibration objective function. However, the performance of the differential evolution algorithm was dependent on the optimisation hyperparameters selected. |
| format | Article |
| id | doaj-art-9618791da3704876bb7ebd21b1feaf00 |
| institution | OA Journals |
| issn | 0264-1275 |
| language | English |
| publishDate | 2024-11-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Materials & Design |
| spelling | doaj-art-9618791da3704876bb7ebd21b1feaf002025-08-20T02:30:46ZengElsevierMaterials & Design0264-12752024-11-0124711340910.1016/j.matdes.2024.113409Calibration and surrogate model-based sensitivity analysis of crystal plasticity finite element modelsHugh Dorward0David M. Knowles1Eralp Demir2Mahmoud Mostafavi3Matthew J. Peel4Department of Mechanical Engineering, University of Bristol, Bristol, BS8 1TR, UK; Corresponding author.Department of Mechanical Engineering, University of Bristol, Bristol, BS8 1TR, UKDepartment of Engineering Science, University of Oxford, Oxford OX1 3PJ, UKDepartment of Mechanical and Aerospace Engineering, Monash University, Clayton, VIC 3800, AustraliaDepartment of Mechanical Engineering, University of Bristol, Bristol, BS8 1TR, UKCrystal plasticity models are powerful tools for predicting the deformation behaviour of polycrystalline materials accounting for underlying grain morphology and texture. These models typically have a large number of parameters, an understanding of which is required to effectively calibrate and apply the model. This study presents a structured framework for the global sensitivity analysis of the effect of crystal plasticity parameters on model outputs. Due to the computational cost of evaluating crystal plasticity models multiple times within a finite element framework, a Gaussian process regression surrogate was constructed and used to conduct the sensitivity analysis. Influential parameters from the sensitivity analysis were carried forward for calibration using both a local Nelder-Mead and global differential evolution optimisation algorithm. The results show that the surrogate based global sensitivity analysis is able to efficiently identify influential crystal plasticity parameters and parameter combinations. Comparison of the Nelder-Mead and differential evolution algorithms demonstrated that only the differential evolution algorithm was able to reliably find the global optimum due to the presence of local minima in the calibration objective function. However, the performance of the differential evolution algorithm was dependent on the optimisation hyperparameters selected.http://www.sciencedirect.com/science/article/pii/S0264127524007846Sensitivity analysisGaussian processSurrogate modelCalibrationCrystal plasticity |
| spellingShingle | Hugh Dorward David M. Knowles Eralp Demir Mahmoud Mostafavi Matthew J. Peel Calibration and surrogate model-based sensitivity analysis of crystal plasticity finite element models Materials & Design Sensitivity analysis Gaussian process Surrogate model Calibration Crystal plasticity |
| title | Calibration and surrogate model-based sensitivity analysis of crystal plasticity finite element models |
| title_full | Calibration and surrogate model-based sensitivity analysis of crystal plasticity finite element models |
| title_fullStr | Calibration and surrogate model-based sensitivity analysis of crystal plasticity finite element models |
| title_full_unstemmed | Calibration and surrogate model-based sensitivity analysis of crystal plasticity finite element models |
| title_short | Calibration and surrogate model-based sensitivity analysis of crystal plasticity finite element models |
| title_sort | calibration and surrogate model based sensitivity analysis of crystal plasticity finite element models |
| topic | Sensitivity analysis Gaussian process Surrogate model Calibration Crystal plasticity |
| url | http://www.sciencedirect.com/science/article/pii/S0264127524007846 |
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