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 relati...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.展开更多
基金the financial contribution from the Natural Sciences and Engineering Research Council of Canada (NSERC), through their Strategic Partnership Grant STPGP 521551in part by support provided by the Digital Research Alliance of Canada (alliance can.ca)
文摘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.
