From Anticosti Island's ‘Deep Time’ to A Cold Spray Additive Manufacturing Art Creation
Bibliographic record
Abstract
Abstract Thermal spray (TS) technology has attracted the attention of numerous industrial sectors due to its apparent simplicity and versatility. It has been used across the world for over 80 years in the conservation and creation of art. Despite the creativity involved in the creation of an art piece, the TS artistic endeavors are limited and insufficiently explored. Unique material combinations, usually not observed in conventional engineering applications, can be achieved with TS technology. Although the material amalgamation possibilities are infinite, their combined deposited characteristics, interfacial compatibility and color palette require further study. In this work, the fields of photography, image processing and TS are combined to produce a large art-piece using the cold gas dynamic spray (CGDS) process. Aluminum, zinc, nickel, alumina, steel and titanium alloy powders are sprayed to replicate in three-dimensions a photograph of a crinoid from the Silurian period found on the Anticosti Island, located in the Gulf of St. Lawrence in Canada. The numerous steps required to produce the artistic 3D piece, namely numerical segmentation of the photograph, conversion to a computer-assisted design (CAD), manufacturing of steel masks and CGDS deposition of the selected powders to reach the sought color palette are described. Powder deposition efficiency, material compatibility and microstructural characteristics are analyzed. and the resulting art piece is presented.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".