README.md

metadata

tags:
  - smart-manufacturing
  - additive-manufacturing
  - sft
license: other
pretty_name: AM12_MeltPoolKinetics-perception
extra_gated_fields:
  Name: text
  Affiliation: text
  Intended use: text
extra_gated_prompt: >-
  This dataset is released for **research use** and access is reviewed and
  granted **manually** by the maintainers. Licensing of the underlying source is
  NOT yet cleared (see the card). Please state your name, affiliation, and
  intended use.

AM12_MeltPoolKinetics-perception

NIR melt-pool ROI frame -> melt-pool state/condition: 4-way {normal,underheat,overheat,edge} (T-B2; edge is a scan-position/boundary condition, not a thermal state) and binary normal/anomaly (T-B1 over non-boundary frames only). 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 melt-pool state/condition 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), but these are not 81k independent images: the export has 45,056 unique input images and 36,043 images appear in both task framings. T-B1 covers only non-boundary frames; the 9,013 edge/turnaround frames from T-B2 are excluded, so binary is a thermal-only normal/anomaly discriminator. Do not map edge to normal or anomaly. In deployment, filter boundary frames using scan position/trajectory metadata or route them to T-B2, where edge is explicit. Only source16 carries genuine per-frame labels. The Hub viewer shows a single train=81,099 container split because the dataset is uploaded as one combined parquet; this is not the recommended training split. Do not use frame-random splits: adjacent frames inside a build are near-duplicates, and the same image can appear once as T-B2 and once as T-B1. Split downstream by metadata.build or metadata.recommended_build_split; the source temporal split is preserved in metadata.source_temporal_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-perception/ + publish/push_am_to_hf.py). Access is gated (manual approval); clear the upstream licence before any onward redistribution.