From microstructure to mechanical properties: Image-based machine learning prediction for AZ80 magnesium alloy

• Deep learning model predicts strength and strain curves from SEM images of AZ80 alloy. • Achieved <2.2 % error in image-out validation for YS and UTS prediction. • Accurate stress-strain curve construction via Ramberg-Osgood predictions. • Synthetic microstructures boost prediction accuracy and balance data classes. • High-res SEM inputs improve predictions, addressing microstructure heterogeneity. Recent advancements in machine learning and computer vision enable direct prediction of mechanical properties from microstructure images. The feasibility of this process hinges on the material structure-property relationship, richness of the dataset, and the choice of machine learning approach. This study investigates the application of a deep learning model to directly predict the yield strength (YS), ultimate tensile strength (UTS), and true stress-strain curve of the cast-forged AZ80 alloys from SEM microstructure images. We manufactured 27 cast-forged AZ80 magnesium alloy components using varied process parameters, creating a diverse dataset of AZ80 microstructures and mechanical properties through their characterization. In addition to predicting magnesium alloy properties, we address challenges related to data imbalance, brightness and contrast variability, and microstructure long-range heterogeneity. We demonstrate that synthetic data oversampling using a denoising diffusion probabilistic model effectively improves the model’s prediction accuracy via balancing the minority classes. A rigorous analysis of the model’s performance shows that the model accurately predicts the YS, UTS, and Ramberg-Osgood equation’s parameters ( K and n ). In image-out validation, the model achieves average percentage errors of 2.10 % (YS), 2.15 % (UTS), 1.50 % ( K ), and 5.47 % ( n ). In class-out validation, the errors are 6.27 %, 9.58 %, 4.69 %, and 10.24 %, respectively.