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AM12_MeltPoolKinetics
NIR melt-pool ROI frame -> thermal state: 4-way {normal,underheat,overheat,edge} (T-B2) and binary normal/anomaly (T-B1, edge excluded). Category B, task T-B2 + T-B1, in the unified Smart-Manufacturing SFT schema.
Records
81,099 records (train=81,099). Model input: a single NIR melt-pool ROI image (196x196), bytes embedded in the image column.
Unified SFT schema (7 fields)
| field | type | meaning |
|---|---|---|
query |
str | the question / instruction (model input) |
image |
Image | null | the INPUT image (bytes embedded) — null for tabular records |
annot |
str | the answer — for this dataset: the thermal-state label (text); the task column selects the 4-way vs binary framing |
reasoning |
null | no native CoT in this dataset |
cate |
"B" | SFT category |
task |
"T-B2 + T-B1" | unified task id |
metadata |
str (JSON) | split, provenance, units, input, license, and (for AM11/13) taxonomy_note |
Notes
Two tasks share one dataset (distinguished by the task column: 45,056 T-B2 + 36,043 T-B1). Only source16 carries genuine per-frame labels. The LOBO split is kept in metadata.lobo_split (not an HF split). Two misfiled non-data PNGs in the source are excluded.
Provenance & licensing
Underlying source: MeltPoolKinetics figshare compilation, source16 = NIST AMMT; DOI 10.6084/m9.figshare.28200101. Upstream license: figshare compilation — per-source terms (source16 = NIST AMMT); verify per source.
Converted read-only into the unified schema; conversion + publish scripts live in
AI4Manufacturing/forge_model
(AM12_MeltPoolKinetics/ + publish/push_am_to_hf.py). Access is gated (manual approval); clear the
upstream licence before any onward redistribution.
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