Effect of Friction-Stir Processing on the Wear Rate of WC-Based MMC Coatings Deposited by Low-Pressure Cold Gas Dynamic Spraying
Bibliographic record
Abstract
Abstract A low-cost, low-pressure (less than 1 MPa) cold spray unit was used to deposit tungsten carbide (WC)-based metal matrix composite (MMC) coatings on low carbon steel substrates. The coatings were then friction-stir processed (FSP) by using a flat cylindrical tool. Scanning electron microscopy (SEM), image analysis, micro-hardness testing, and ASTM Standard G65 dry abrasion wear testing were conducted to study the influence of FSP on the coating properties and its wear rate. It was found that porosity increased following FSP on the coating due to insufficient flow of the metal matrix material (nickel). The hardness of the WC-based MMC coating decreased after FSP as a result of increase in porosity and possible decarburization of the WC caused by the heat of the FSP. The SEM images taken from the cross sections of the FSPed coatings confirmed the effectiveness of FSP in distributing the WC particles within the matrix to produce a coating with uniform distribution of WC particles in the matrix. As a result, the abrasion wear resistance of the coatings after FSP increased compared to that of the as-sprayed coatings. This suggested that FSP can be considered as a method to improve the wear properties of MMC coatings.
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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".