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Study on High-Throughput Inversion Method for Anisotropic Material Parameters Based on Nanoindentation.
- Source :
-
Experimental Mechanics . Sep2023, Vol. 63 Issue 7, p1157-1170. 14p. - Publication Year :
- 2023
-
Abstract
- 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. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 00144851
- Volume :
- 63
- Issue :
- 7
- Database :
- Academic Search Index
- Journal :
- Experimental Mechanics
- Publication Type :
- Academic Journal
- Accession number :
- 172439297
- Full Text :
- https://doi.org/10.1007/s11340-023-00977-4