Experimental Validation and Calibration of Computationally Efficient Mesoscale Microstructure Model for LPBF-Processed Ti-6Al-4V

Modeling additively manufactured parts under varying process conditions is critical for predicting microstructure and mechanical properties. These predictive capabilities facilitate process qualification and certification by enabling the development of digital twins, which in turn improve confidence in part performance while reducing operational costs associated with machine time and raw materials. Various modeling approaches have been developed to capture the complex interactions of process parameters and underlying physical phenomena in LPBF. However, achieving both high computational efficiency and predictive accuracy remains a challenge, particularly with the increasing integration of machine learning techniques. In this study, we employ the novel, computationally efficient mesoscale microstructure Pass Scale simulation model to generate as-printed microstructures of Ti-6Al-4V under different LPBF process conditions. The simulated microstructures are validated against experimental EBSD data, with comparisons of key microstructural features, including texture, grain size, aspect ratio and grain boundary characteristics. This validation provides critical insights into the model’s accuracy and establishes a foundation for further refinement and integration with higher-fidelity, computationally intensive modeling approaches to enhance efficiency and predictive capabilities.