About this Abstract |
Meeting |
2023 TMS Annual Meeting & Exhibition
|
Symposium
|
AI/Data Informatics: Computational Model Development, Validation, and Uncertainty Quantification
|
Presentation Title |
Extraction of Creep Parameters from Indentation Creep Experiment: An Artificial Neural Network-Based Approach |
Author(s) |
Raj Jung Mahat, Vikram Jayaram, Praveen Kumar |
On-Site Speaker (Planned) |
Praveen Kumar |
Abstract Scope |
Indentation creep is a faster alternative to conventional uniaxial creep testing with an added advantage of small probing volume; however, interpretation of indentation creep data in terms of uniaxial creep response is challenging due to the complex stress field below the indenter. Here, a fully-connected sequential multi-layered artificial neural network (ANN), trained using finite element (FE) indentation creep simulations, was used to map the displacement-time response obtained from indentation creep experiments to the corresponding uniaxial creep parameters. ANN was trained using a back-propagation algorithm based on a batch-gradient descent to map the indentation displacement-time inputs and the uniaxial creep parameters used in the FE simulations. The trained ANN was tested on the nanoindentation creep data of commercial purity Pb at room temperature, and its prediction was compared with the uniaxial creep parameters. A match between the experimental data and the ANN prediction for stress exponent and time exponent was noted. |
Proceedings Inclusion? |
Planned: |
Keywords |
Machine Learning, Mechanical Properties, Modeling and Simulation |