No abstract

In this study, a micromechanical finite element model is proposed based on experimental data and the rule of the mixture (RoM) in order to predict the tensile behavior of mechanical properties of heat-treated dual-phase medical-grade titanium (Ti–6Al–4V). Tensile tests, micro-hardness measurements, and RoM were used to obtain flow curves for the α and β phases. Scanning electron microscopy (SEM) imaging was used to determine phase fractions and to create representative volume elements (RVEs). Furthermore, the Gurson-Tvergaard-Needleman (GTN) damage model was calibrated using the Taguchi design of experiment (DOE) method in order to predict damage in the microstructure and the results were compared to fracture surface obtained using fractography in order to investigate failure mechanisms. The final micromechanical model could accurately predict stress-strain curves and showed that void formation and coalescence is the primary mechanism of failure. Finally, analyses of the surfaces showed that a fully ductile fracture occurs at the failure point, which agrees with the results of the damage model. The results suggest that the proposed model can predict the failure of heat-treated Ti–6Al–4V bio-alloys.

A comprehensive approach to understand the mechanical behavior of materials involves costly and time-consuming experiments. Recent advances in machine learning and in the field of computational material science could significantly reduce the need for experiments by enabling the prediction of a material’s mechanical behavior. In this paper, a reliable data pipeline consisting of experimentally validated phase field simulations and finite element analysis was created to generate a dataset of dual-phase steel microstructures and mechanical behaviors under different heat treatment conditions. Afterwards, a deep learning-based method was presented, which was the hybridization of two well-known transfer-learning approaches, ResNet50 and VGG16. Hyper parameter optimization (HPO) and fine-tuning were also implemented to train and boost both methods for the hybrid network. By fusing the hybrid model and the feature extractor, the dual-phase steels’ yield stress, ultimate stress, and fracture strain under new treatment conditions were predicted with an error of less than 1%.