Bibliographic record
Abstract
Cold spray additive manufacturing is based on the idea that high-velocity particles transfer their kinetic energy to the substrate. A converging-diverging de Laval nozzle produces a supersonic gas stream, which is used in this operation. The feedstock powder is then introduced into the gas stream via a carrier gas, with the gas stream being guided in that direction. The powder particles are impacted and deformed by the high-velocity gas particles, flattening and adhering to the substrate as a result. The development of a 3D structure results from the accumulation of successive layers of powder. Unlike other additive manufacturing processes, cold spray additive manufacturing does not require high temperatures, making it suitable for a wide range of materials and applications. Aerospace, automotive, biomedical, and electronics components may all be repaired, modified, and made from scratch using cold spray additive manufacturing. In this chapter, a detailed overview of cold spray additive manufacturing, including principles, applications, and recent advancements are presented. Detailed relevant literature on this topic and discuss the key challenges and opportunities in this field is presented. Chapter concludes with a discussion of the potential future directions and applications of cold spray additive manufacturing.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.018 | 0.011 |
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".