Scanning Electron Microscopy (SEM) Dataset of Additively Manufactured Ni-WC Metal Matrix Composites for Semantic Segmentation
Bibliographic record
Abstract
This dataset accompanies the publication “Accelerated quantification of reinforcement degradation in additively manufactured Ni-WC metal matrix composites via SEM and vision transformers.” It contains scanning electron microscopy (SEM) images and corresponding pixel-level segmentation masks used to train, validate, and test deep learning models for microstructural analysis. The images represent metallographic cross-sections of directed energy deposited (DED) nickel-tungsten carbide (Ni-WC) metal matrix composites (MMCs), prepared through a detailed process involving sectioning, polishing, and etching. Each SEM image highlights key microstructural constituents — matrix, carbide particles, dilution bands, and reprecipitated carbides — with the latter two representing degradation features of reinforcement particles under elevated thermal conditions. The dataset includes: AugmentedImages.zip: 405 SEM image crops (512×512 pixels each) obtained from four magnification levels (1000×, 800×, 700×, 600×) and augmented via flips, rotations, elastic transforms, grid distortions, and controlled contrast/brightness variations. AugmentedMasks.zip: Corresponding manually labeled segmentation masks, generated using the Supervisely interface with pixel-level accuracy and verified for class consistency. The data were generated and augmented using the Albumentations library to enhance generalization and support high-throughput segmentation studies. The dataset enables benchmarking and comparison of convolutional (e.g., DeepLabV3+) and transformer-based architectures (e.g., SegFormer, UPerNet, Mask2Former, etc.) for microstructure segmentation in additive manufacturing contexts. Applications: Training and evaluation of semantic segmentation models for materials characterization Quantification of carbide degradation and process-induced defects in Ni-WC MMCs Benchmarking of vision transformer architectures on SEM datasets File format: Images: .bmp Masks: .bmp Resolution: 512×512 pixels Acknowledgment:If you use this dataset, please cite the associated article and acknowledge the dataset authors.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.021 | 0.025 |
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".