Datasets:

AI4Manufacturing
/

AM09_DED_energy

tags:
  - smart-manufacturing
  - additive-manufacturing
  - sft
license: cc-by-sa-4.0
pretty_name: AM09_DED_energy
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.

AM09_DED_energy

DED coaxial melt-pool frame -> laser-energy level P1-P4 (4-class image classification). Category B, task T-B2, in the unified Smart-Manufacturing SFT schema.

Records

62,194 records (train=62,194). Model input: a single coaxial VIS melt-pool image (590x480), 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 energy-level label — one of `P1,P2,P3,P4` (text)
`reasoning`	null	no native CoT in this dataset
`cate`	"B"	SFT category
`task`	"T-B2"	unified task id
`metadata`	str (JSON)	split, provenance, units, `input`, license, and (for AM11/13) `taxonomy_note`

Notes

Uploaded as ONE dataset (all rows together); the original train/test partition lives in metadata.split (train 43,463 / test 18,731), NOT as HF splits. The label is the source folder (P1-P4), verified by viewing pixels (P1 small/dim ... P4 large + halo).

Provenance & licensing

Underlying source: DED coaxial melt-pool imaging, Zenodo DOI 10.5281/zenodo.10421423. Upstream license: CC-BY-SA-4.0 (ShareAlike/viral) + CC-BY-4.0 (mixed on Zenodo). Converted read-only into the unified schema; conversion + publish scripts live in AI4Manufacturing/forge_model (AM09_DED_energy/ + publish/push_am_to_hf.py). Access is gated (manual approval); clear the upstream licence before any onward redistribution.