A multi-source melt pool compilation for vision-based analytics applications in additive manufacturing
Bibliographic record
Abstract
Additive manufacturing (AM) fabricates physical objects by layering materials from a 3D digital model. In metallic AM, the melt pool represents a region of superheated molten material and plays a critical role in determining part quality. Monitoring this region has proven valuable for downstream analytics such as defect detection and microstructure prediction. Melt pool signatures, represented through various modalities (e.g., visual, thermal, acoustic) and metrics (e.g., temperatures, gradients, rates), capture essential process patterns. However, the diversity of materials, process parameters, and sensing configurations across AM systems has limited standardization. To address this, we introduce Melt-Pool-Kinetics, a curated dataset compiled from 32 datasets across 23 sources, totaling 1.9 TB of raw data and released as a 48.6 GB HDF5 collection. Images were processed using cropping, centering, resizing, grayscaling, denoising, and debayering techniques. The dataset can support machine learning applications for in-situ monitoring, process optimization, and control. The dataset is structured into different levels, such as raw, processed, and diverse subsets. The dataset enables future expansion as new melt pool data becomes available.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 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".