Deposition of Copper by Cold Gas Dynamic Spraying: an Investigation of Dependence of Microstructure and Properties of the Deposits on the Spraying Conditions
Bibliographic record
Abstract
Abstract In the Cold Gas Dynamic Spray (CGDS) process, coatings are deposited by the virtue of the high particle velocity achieved by the use of converging-diverging (de Laval) nozzle along with suitable particle characteristics and process parameters. In this study copper coatings were deposited on aluminium substrates using helium as the accelerating gas. The influence of the CGDS conditions, primarily driving gas temperature and pressure, on the nature of the deposited coatings and the deposition efficiency of the process were investigated. The results indicate that it is possible to deposit copper coatings at a wide range of process conditions, with successful deposition being observed with the driving gas at room temperature and 11 bar pressure (a condition where the nozzle is still choked). However, the nature of the coatings is strongly dependent upon the processing conditions. With room temperature driving gas, an increase in pressure lead to an increase in deposition efficiency, and increase in substrate deformation and an increase in microhardness in the deposit due to higher levels of work hardening. The use of driving gas at temperatures as low as 473 K resulted in recrystallisation in the deposit and a decrease in tendency to debond due to stress relief during recrystallisation. Recrystallisation also manifested itself in reduced hardness. The sensitivity of the recrystallisation conditions to the traverse speed of the jet over the substrate indicated that these processes are initiated by the impingement of the hot gas jet onto the deposit following deposition and not by changes in velocity or temperature of the particles upon impact.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".