Prediction of uniaxial tensile flow using finite element-based indentation and optimized artificial neural networks

This study derives a uniaxial tensile flow from spherical indentation data using an artificial neural network (ANN) combined with finite element (FE) analysis. The feasibility of the FE-based simulations is confirmed through experimental indentation for various steels. Parametric studies of the FE simulation are performed to generate an ANN training database. An encoding for feature extraction and a hyperparameter optimization is implemented to design the ANN with high predictive performance. The indentation load–depth curves are converted into hardening parameters through the trained ANN. The predictive performance of the FE–ANN model using real-life indentation data is investigated in-depth with thorough error evaluation, and verified by uniaxial tensile tests. The emphasis is made that the mean absolute percentage error between the experimental and simulated indentation data is required to be meticulously controlled below 1% to accurately predict the tensile properties. The validations demonstrate that the applied FE–ANN modeling approach is very robust and captures the tensile properties well. Furthermore, the Taguchi orthogonal array (OA) method that can achieve high efficiency and fidelity with less training data is discussed. The FE–ANN model is concisely designed using the Taguchi OA method and can predict elasticity as well as plasticity. [Display omitted] •The finite element-Artificial neural network (FE-ANN) modeling approach is used to predict uniaxial tensile flow from real indentation data.•Encoding of input features and hyperparameter optimization are performed for the high performance of the ANN.•The FE-ANN model performance is outstanding when the error between measured and simulated indentation data is meticulously controlled.•Taguchi orthogonal array (OA) method provides high efficiency and accuracy in prediction with reduced training data pairs.•The concisely designed FE-ANN model using the Taguchi OA can cover broader parameter spaces in limited memories.