Research on a Multi-stage Progressive Identification Method for the Constitutive Parameters of the Duplex Steels′s Physical-based Crystal Plasticity Model under Small Sample Conditions

doi:10.3901/JME.260561

Abstract

Abstract: The physically-based crystal plasticity constitutive model is widely applied to study the plastic behavior of duplex steels. However，due to the difficulty in separately determining the mechanical properties of the constituent phases of duplex steels，and the strong coupling between the parameters of the physically-based crystal plasticity model，parameter calibration becomes challenging. To address this issue，based on deep learning algorithms，a neural network model is constructed to identify the parameters of a physically-based crystal plasticity model describing the plastic behavior of duplex steels. The model utilizes the macroscopic true stress-true strain curve of the material and the dislocation densities of the two phases at different deformation stages as input data. For duplex steels without texture strengthening，a uniaxial tensile numerical simulation model is developed by combining crystal plasticity finite element analysis and the representative volume element method. The simulation results provide the sample database required for model training. To overcome the poor parameter identification performance typically observed in conventional neural networks，a multi-stage progressive identification method is proposed. The method first decouples the parameters，then identifies them in stages based on their impact on material properties. This approach improves the identification accuracy while effectively reducing the required sample size. Validation test results，including both validation set data and experimental data，demonstrate that the proposed multi-stage progressive parameter identification method can accurately obtain the physically-based crystal plasticity model parameters for duplex steel under small sample conditions. This method provides a feasible approach for the accurate identification of the parameters of the crystal plasticity constitutive model in duplex steels.

Key words: crystal plasticity, duplex steel, parameter identification, small sample, multi-stage progressive identification

CLC Number:

TG156

LI Xiaolong, WU Haopeng, QIN Rui, CHEN Lei, JIN Miao, ZHANG Qingling. Research on a Multi-stage Progressive Identification Method for the Constitutive Parameters of the Duplex Steels′s Physical-based Crystal Plasticity Model under Small Sample Conditions[J]. Journal of Mechanical Engineering, 2026, 62(12): 163-172.

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URL: http://www.cjmenet.com.cn/EN/10.3901/JME.260561

http://www.cjmenet.com.cn/EN/Y2026/V62/I12/163

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