From microstructure to mechanical properties: Image-based machine learning prediction for AZ80 magnesium alloy
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".