Interpretable Calibration of Crystal Plasticity Model Using a Bayesian Surrogate-Assisted Genetic Algorithm
The accurate calibration of material parameters in crystal plasticity models is essential for applying crystal plasticity (CP) simulations. Identifying these parameters usually requires unfeasible single-crystal experiments or expensive time costs due to the use of traditional genetic algorithm (GA)...
| Main Authors: | , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2023-01-01
|
| Series: | Metals |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2075-4701/13/1/166 |
| _version_ | 1797438678725820416 |
|---|---|
| author | Shuaiyi Yang Xuefeng Tang Lei Deng Pan Gong Mao Zhang Junsong Jin Xinyun Wang |
| author_facet | Shuaiyi Yang Xuefeng Tang Lei Deng Pan Gong Mao Zhang Junsong Jin Xinyun Wang |
| author_sort | Shuaiyi Yang |
| collection | DOAJ |
| description | The accurate calibration of material parameters in crystal plasticity models is essential for applying crystal plasticity (CP) simulations. Identifying these parameters usually requires unfeasible single-crystal experiments or expensive time costs due to the use of traditional genetic algorithm (GA) optimization. This study proposed an efficient and interpretable method for calibrating the constitutive parameters with macroscopic mechanical tests. This approach utilized the Bayesian neural network (BNN)-based surrogate-assisted GA (SGA) optimization method to identify a group of constitutive parameters that can reproduce the experimental stress–strain curve and crystallographic orientation by crystal plasticity simulation. The proposed approach was performed on the calibration of typical high-entropy alloy material parameters in two different CP models. The use of the surrogate model reduces the call count of simulation in the parameter searching process and speeds up the calibration significantly. With the help of infill sampling, the accuracy of this optimization method is consistent with the CP simulation and not limited by the accuracy of the surrogate model. Another merit of this method is that the pattern that the BNN surrogate found in the model parameters can be interpreted with its integrated gradients, which helps us to understand the relationship between constitutive parameters and the output mechanical response. The interpretation of BNN can guide further experiment design to decouple particular parameters and add constraints provided by the attached experiment or prior knowledge. |
| first_indexed | 2024-03-09T11:41:43Z |
| format | Article |
| id | doaj.art-a9a2016304f4460f830735a39d04ea25 |
| institution | Directory Open Access Journal |
| issn | 2075-4701 |
| language | English |
| last_indexed | 2024-03-09T11:41:43Z |
| publishDate | 2023-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Metals |
| spelling | doaj.art-a9a2016304f4460f830735a39d04ea252023-11-30T23:31:39ZengMDPI AGMetals2075-47012023-01-0113116610.3390/met13010166Interpretable Calibration of Crystal Plasticity Model Using a Bayesian Surrogate-Assisted Genetic AlgorithmShuaiyi Yang0Xuefeng Tang1Lei Deng2Pan Gong3Mao Zhang4Junsong Jin5Xinyun Wang6State Key Laboratory of Materials Processing and Die and Mold Technology, School of Material Science and Engineering, Huazhong University of Science and Technology, Wuhan 430074, ChinaState Key Laboratory of Materials Processing and Die and Mold Technology, School of Material Science and Engineering, Huazhong University of Science and Technology, Wuhan 430074, ChinaState Key Laboratory of Materials Processing and Die and Mold Technology, School of Material Science and Engineering, Huazhong University of Science and Technology, Wuhan 430074, ChinaState Key Laboratory of Materials Processing and Die and Mold Technology, School of Material Science and Engineering, Huazhong University of Science and Technology, Wuhan 430074, ChinaState Key Laboratory of Materials Processing and Die and Mold Technology, School of Material Science and Engineering, Huazhong University of Science and Technology, Wuhan 430074, ChinaState Key Laboratory of Materials Processing and Die and Mold Technology, School of Material Science and Engineering, Huazhong University of Science and Technology, Wuhan 430074, ChinaState Key Laboratory of Materials Processing and Die and Mold Technology, School of Material Science and Engineering, Huazhong University of Science and Technology, Wuhan 430074, ChinaThe accurate calibration of material parameters in crystal plasticity models is essential for applying crystal plasticity (CP) simulations. Identifying these parameters usually requires unfeasible single-crystal experiments or expensive time costs due to the use of traditional genetic algorithm (GA) optimization. This study proposed an efficient and interpretable method for calibrating the constitutive parameters with macroscopic mechanical tests. This approach utilized the Bayesian neural network (BNN)-based surrogate-assisted GA (SGA) optimization method to identify a group of constitutive parameters that can reproduce the experimental stress–strain curve and crystallographic orientation by crystal plasticity simulation. The proposed approach was performed on the calibration of typical high-entropy alloy material parameters in two different CP models. The use of the surrogate model reduces the call count of simulation in the parameter searching process and speeds up the calibration significantly. With the help of infill sampling, the accuracy of this optimization method is consistent with the CP simulation and not limited by the accuracy of the surrogate model. Another merit of this method is that the pattern that the BNN surrogate found in the model parameters can be interpreted with its integrated gradients, which helps us to understand the relationship between constitutive parameters and the output mechanical response. The interpretation of BNN can guide further experiment design to decouple particular parameters and add constraints provided by the attached experiment or prior knowledge.https://www.mdpi.com/2075-4701/13/1/166interpretable calibrationcrystal plasticityBayesian surrogategenetic algorithmBayesian neural network |
| spellingShingle | Shuaiyi Yang Xuefeng Tang Lei Deng Pan Gong Mao Zhang Junsong Jin Xinyun Wang Interpretable Calibration of Crystal Plasticity Model Using a Bayesian Surrogate-Assisted Genetic Algorithm Metals interpretable calibration crystal plasticity Bayesian surrogate genetic algorithm Bayesian neural network |
| title | Interpretable Calibration of Crystal Plasticity Model Using a Bayesian Surrogate-Assisted Genetic Algorithm |
| title_full | Interpretable Calibration of Crystal Plasticity Model Using a Bayesian Surrogate-Assisted Genetic Algorithm |
| title_fullStr | Interpretable Calibration of Crystal Plasticity Model Using a Bayesian Surrogate-Assisted Genetic Algorithm |
| title_full_unstemmed | Interpretable Calibration of Crystal Plasticity Model Using a Bayesian Surrogate-Assisted Genetic Algorithm |
| title_short | Interpretable Calibration of Crystal Plasticity Model Using a Bayesian Surrogate-Assisted Genetic Algorithm |
| title_sort | interpretable calibration of crystal plasticity model using a bayesian surrogate assisted genetic algorithm |
| topic | interpretable calibration crystal plasticity Bayesian surrogate genetic algorithm Bayesian neural network |
| url | https://www.mdpi.com/2075-4701/13/1/166 |
| work_keys_str_mv | AT shuaiyiyang interpretablecalibrationofcrystalplasticitymodelusingabayesiansurrogateassistedgeneticalgorithm AT xuefengtang interpretablecalibrationofcrystalplasticitymodelusingabayesiansurrogateassistedgeneticalgorithm AT leideng interpretablecalibrationofcrystalplasticitymodelusingabayesiansurrogateassistedgeneticalgorithm AT pangong interpretablecalibrationofcrystalplasticitymodelusingabayesiansurrogateassistedgeneticalgorithm AT maozhang interpretablecalibrationofcrystalplasticitymodelusingabayesiansurrogateassistedgeneticalgorithm AT junsongjin interpretablecalibrationofcrystalplasticitymodelusingabayesiansurrogateassistedgeneticalgorithm AT xinyunwang interpretablecalibrationofcrystalplasticitymodelusingabayesiansurrogateassistedgeneticalgorithm |