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This study primarily aims to develop a robust modelling approach to capture complex material behavior of CP-Ti, appeared by high anisotropy, differential hardening due to anisotropy evolution, and flow behavior sensitive to strain rate and temperature, using artificial neural networks (ANNs). Plasticity is characterized by uniaxial tension and in-plane biaxial tension tests at temperatures of 0°C and 20°C with strain rates of 0.001 /s and 0.01 /s, and the results are used to calibrate the non-quadratic anisotropic Yld2000-3d yield function with respect to the plastic work. In order to predict the intricate plastic deformation with the temperature and strain rate effects, two distinct ANN models are developed; one is to capture the strain hardening behavior and the other to predict the anisotropic parameters in the chosen yield function. The developed ANN models predict an unseen dataset well, which is intermediate testing conditions at a temperature of 10°C and strain rate of 0.005 /s. The ANN models, being computationally stable and adhering to conventional constitutive equations, are implemented into a user material subroutine for the ductile fracture characterization of CP-Ti sheet using the hybrid experimental-numerical analysis. The favorable agreement between experimental data and numerical predictions, particularly using the ANN models with evolving anisotropic material parameters for the Yld2000-3d yield function, underscores the significance of differential hardening effect on the ductile fracture behavior and highlights the capabilities of ANN models to capture the complex plastic behavior of CP-Ti. The key parameters including stress triaxiality, Lode angle parameter, and equivalent plastic strain at the fracture location are extracted from the simulations, enabling the calibration of ductile fracture models, namely Johnson-Cook, Hosford-Coulomb, and Lou-2014, and construction of fracture envelopes.
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Identification of Yld2000–2d anisotropic yield function parameters from single hole expansion test using machine learning
This study presents a novel machine learning approach for predicting the anisotropic parameters of the Yld20002d non-quadratic yield function using a hole expansion test. Heterogeneous stress-strain fields during the test substitute for multiple experiments required in the conventional parameter identification approach. An artificial neural network model for the parameter prediction is developed using a virtually generated training dataset composed of strains from hole expansion simulations, performed using randomly selected anisotropic parameters. The developed model predicts the Yld20002d parameters for AA6022-T4 based on the measured strain field from a hole expansion experiment, and the parameter results are evaluated by comparing anisotropy in uniaxial tension tests, the yield locus, and thinning variation in hole expansion test.
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- Award ID(s):
- 1757371
- PAR ID:
- 10519338
- Publisher / Repository:
- Elsevier
- Date Published:
- Journal Name:
- CIRP Annals
- ISSN:
- 0007-8506
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1016/j.cirp.2024.04.026
