Multi-scale simplified residual convolutional neural network model for predicting compositions of binary magnesium alloys
Bibliographic record
Abstract
This study proposes a multi-scale simplified residual convolutional neural network (MS-SRCNN) for the precise prediction of Mg-Nd binary alloy compositions from scanning electron microscope (SEM) images. A multi-scale data structure is established by spatially aligning and stacking SEM images at different magnifications. The MS-SRCNN significantly reduces computational runtime by over 90 % compared to traditional architectures like ResNet50, VGG16, and VGG19, without compromising prediction accuracy. The model demonstrates more excellent predictive performance, achieving a >5 % increase in R 2 compared to single-scale models. Furthermore, the MS-SRCNN exhibits robust composition prediction capability across other Mg-based binary alloys, including Mg-La, Mg-Sn, Mg-Ce, Mg-Sm, Mg-Ag, and Mg-Y, thereby emphasizing its generalization and extrapolation potential. This research establishes a non-destructive, microstructure-informed composition analysis framework, reduces characterization time compared to traditional experiment methods and provides insights into the composition-microstructure relationship in diverse material systems.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 source (direct Gemma or distilled Codex), 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".