Role of tamping effect in wear behavior of Cu-TiC composite coatings deposited by cold spray
Bibliographic record
Abstract
In the cold spray process, tamping refers to the effect where impacting cold-sprayed particles modify the microstructure and properties of previously deposited materials. The effect of tamping on wear performance was studied for cold sprayed ceramic-reinforced metal matrix composite (MMC) coatings. A Cu coating and three Cu-TiC MMC coatings were deposited, using three feedstocks containing 0, 10 (Cu-10cT) and 70 wt% (Cu-70cT) of coarse TiC powder and one feedstock containing 10 wt% of fine TiC powder (Cu-10fT), respectively. Mechanical properties were evaluated using multi-scale indentation and scratch bond strength testing. X-ray diffraction and electron backscatter diffraction were employed to examine the deformation in the coatings over large and small length scales, respectively. Tribological properties in dry air and nitrogen environments were studied in sliding wear using a ball-on-disk tribometer. Chemical composition on wear tracks was examined by Raman spectroscopy. The deposition efficiency decreased with the increasing TiC ratio for Cu, Cu-10cT and Cu-70cT coatings. Low deposition efficiency manifested as enhanced tamping led to decreased porosity, elevated deformation level, increased micro- and nano-hardness, and improved cohesion strength, resulting in enhanced wear resistance. The lowest wear rate was observed for Cu-70cT. The Cu-10fT showed a similar tamping effect to Cu-70cT. However, the fine TiC particles were uniformly dispersed along the Cu particle-particle interfaces in Cu-10fT, compromising its cohesion strength and leading to easier removal of coating materials during wear test. In nitrogen, adhesion between first bodies was reduced due to the absence of Cu oxide-containing tribolayers. The coatings except for Cu-70cT exhibited lower wear rates in nitrogen than those in dry air. Cu-70cT showed much lower wear rate in dry air than in nitrogen, indicating the important role of the stable Cu 2 O-containing tribolayers in reducing wear.
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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 | 0.001 |
| 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".