Mechanical Behavior and Sliding Wear Studies on Iron Aluminide Coatings Reinforced with Titanium Carbide
Bibliographic record
Abstract
Wear-resistant iron aluminide-based composites were coated on steel substrates with the High-Velocity Oxy-Fuel (HVOF) technique using ball milled Fe3Al and TiC powders as feedstock. The phase composition, microstructure, microhardness, elastic modulus and dry sliding wear performance of unreinforced Fe3Al and Fe3Al–TiC composite coatings (reinforced with 30 and 50 vol. % TiC particles) were evaluated in order to reveal the relationship between the mechanical and tribological behaviors. Compared to the unreinforced coatings, the composite coating with 30 vol. % TiC particles exhibited much greater hardness and higher elastic modulus. The increase of the elastic modulus of the composite coatings did not result in deterioration of sliding wear behavior. The addition of 50 vol. % TiC resulted in a further increase in hardness, however, both composite coatings showed the same elastic modulus. The fractured cross sectional surface of the unreinforced coating showed a weakly bonded microstructure promoting delamination in wear tests, whereas the composite fractured surface showed strong mechanical bonding between the matrix and carbide particles, leading to better cohesion. The Fe3Al–TiC coatings showed almost three orders of magnitude higher wear resistance under the dry sliding wear test compared to the unreinforced 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 | 0.000 |
| 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".