PolyCSAM: boosting Cold Spray Additive Manufacturing into full-scale production: a joint Canadian venture launches a new facility with an innovative hybrid approach to cold spray additive manufacturing
Bibliographic record
Abstract
The global market for 3D printing, or additive manufacturing, of metal-based parts was estimated to be US $774 million in 2019 and is projected to reach over US $3 billion by 2024, yielding a compound annual growth rate (CAGR) of 32.5% from 2019 to 2024[1] with powder bed fusion being the dominant technology. Cold spray is a powder metal consolidation technique that has been primarily recognized as a coating process [2-3]. However, it has core attributes common to conventional direct energy deposition metal additive manufacturing (AM), with the important distinction of operating at comparatively "cold" conditions. Instead of melting the powder or wire, the metal powders in the cold spray process are accelerated to supersonic velocities through a nozzle aimed at the point of deposition. Upon impact of the particles, the high kinetic energy causes plastic deformation for mechanical interlocking and metallurgical bonding as a layer of material builds up. Practically, at temperatures far below the melting point, this solid state process prevents undesirable material transformation such as phase changes, oxidation, chemical reactions, or cracking, commonly observed in most metal AM processes involving melting and materials undergoing rapid solidification. The low heat input in the cold spray process further reduces thermal load effects on the substrate, enabling repairs without inducing undue stresses in the substrates [4-5]. Moreover, it is well suited for multi-material buildups and generates high "build" rates, thus overcoming the slow productivity of conventional AM processing. Finally, the cold spray process operates under normal atmospheric conditions and is thereby inherently scalable to large parts as no inert or vacuum printing environment is required. These characteristics of cold spray give rise to a new degree of freedom to AM that leads to innovation in manufacturing.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.015 | 0.005 |
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".