Study on High-Throughput Inversion Method for Anisotropic Material Parameters Based on Nanoindentation

Background: Accurate measurements of material constitutive model parameters are of great significance for design optimization and reliability analysis. Objective: In this paper, to characterize the anisotropic elastoplastic parameters of single-crystal metal materials at the nanoscale, a high-throughput inversion method of anisotropic elastoplastic constitutive parameters of single-crystal metal materials using a neural network and bicrystalline indentation load-depth curve is proposed. It addresses the limitations of indentation technology in the characterization of anisotropic material parameters. Methods: A large number of finite-element simulation results were used to build a sample dataset. A neural network was used to build a mapping relationship model between the characteristics of the indentation load-depth curve and the parameters of the material elastic–plastic constitutive model. Results: The parameter inversion method based on the neural network reduced the iterative optimization link, improved the parameter inversion efficiency, and realized high-throughput parameter inversion of the nonupdated intelligent material constitutive model. Conclusion: The effectiveness of the method was verified by inversion experiments of anisotropic elastic–plastic parameters of a single-crystal copper material. The accuracy and universality of the method were further verified by an error analysis, demonstrating the engineering application prospects of the proposed method.