To address the challenge of evaluating the internal stress of materials or structures in service environments, a method combining finite element analysis and micro-indentation testing is proposed. Taking the CoCrFeNiMn high-entropy alloy as the research object, compression, shear and micro-indentation tests were carried out at various loading speeds respectively. Based on an asymmetric initial yield function, Swift hardening and the associated flow rule, an elastoplastic constitutive model for this material was established. The constitutive model was programmed by using the stress integration algorithm and interfaced with the ABAQUS finite element software. Furthermore, by comparing the finite element simulation results with the experimental results from the split Hopkinson pressure bar (SHPB) tests and the indentation model, the reliability of the model was verified. Using the SHPB model, the numerical simulation of the dynamic compression experiment was carried out, and the stress fields at different dynamic deformation moments were imported into the indentation model as the initial stress (internal stress) fields for indentation simulation analysis. The results indicated that the initial stress field in the loading stage significantly reduces the indentation load at the same indentation depth, and the reduction amplitude increases with the increase of stress. In addition, the existence of the initial stress field will further weaken the stress concentration during the indentation process. Through the quantitative analysis of the load-indentation displacement curves under different compression amounts, the indentation response laws of the materials under different initial stress conditions were revealed. The research results provide a reference for the evaluation of the internal stress of materials or structures under service conditions.

Machine learning techniques have been increasingly applied to the prediction of material behavior and have demonstrated clear advantages over conventional constitutive modeling approaches. The objective of this study was to develop an accurate and computationally efficient data-driven constitutive description for metallic materials under coupled temperature and strain-rate loading conditions. A CoCrFeNiMn high-entropy alloy was selected as the representative material system.Compression experiments were performed over a wide range of temperatures and strain rates to obtain true stress-strain data. Based on the experimental results, a modified Johnson-Cook constitutive model was calibrated to describe strain hardening, strain-rate sensitivity, and thermal softening effects. The calibrated model was then implemented in finite element simulations to generate a large, physically consistent dataset spanning broad thermo-mechanical conditions. This simulation-assisted data generation strategy expanded the training domain while ensuring continuity and stability of the dataset. Using the generated data, an artificial neural network (ANN) model was constructed to learn the nonlinear relationship between strain, strain rate, temperature, and flow stress. The network architecture and training strategy were optimized to improve prediction accuracy and generalization performance. To enable efficient application of the trained ANN within an explicit finite element framework, an automatic FORTRAN code generation tool was developed. The trained ANN parameters were converted into a user-defined material subroutine and embedded into the Abaqus/Explicit platform, allowing direct numerical implementation without external dependencies.The results indicate that the ANN-based constitutive model predicts flow stress with high accuracy, with relative errors remaining below one percent across the investigated loading conditions. In addition, the ANN implementation exhibits higher computational efficiency than the conventional constitutive model in explicit finite element simulations.It is concluded that the data-driven neural network approach can effectively replace traditional phenomenological constitutive models in finite element analysis. The proposed framework provides an efficient and reliable pathway for numerical modeling and simulation of metallic materials under complex thermo-mechanical conditions.