A machine learning method to quantitatively predict alpha phase morphology in additively manufactured Ti-6Al-4V

Abstract

Abstracts Quantitatively defining the relationship between laser powder bed fusion (LPBF) process parameters and the resultant microstructures for LPBF fabricated alloys is one of main research challenges. To date, achieving the desired microstructures and mechanical properties for LPBF alloys is generally done by time-consuming and costly trial-and-error experiments that are guided by human experience. Here, we develop an approach whereby an image-driven conditional generative adversarial network (cGAN) machine learning model is used to reconstruct and quantitatively predict the key microstructural features (e.g., the morphology of martensite and the size of primary and secondary martensite) for LPBF fabricated Ti-6Al-4V. The results demonstrate that the developed image-driven machine learning model can effectively and efficiently reconstruct micrographs of the microstructures within the training dataset and predict the microstructural features beyond the training dataset fabricated by different LPBF parameters (i.e., laser power and laser scan speed). This study opens an opportunity to establish and quantify the relationship between processing parameters and microstructure in LPBF Ti-6Al-4V using a GAN machine learning-based model, which can be readily extended to other metal alloy systems, thus offering great potential in applications related to process optimisation, material design, and microstructure control in the additive manufacturing field.