Wear Behaviour of Al-based Composite Coatings Obtained by Laser Cladding and Reinforced with WC, TiC and SiC Particles
Bibliographic record
Abstract
The present study investigates the influence of the composition and the reinforcement nature of laser cladded metal matrix composite (MMC) coatings on the hardness and adhesive wear behaviour. Laser cladding was carried out on an Al–Si–Mg substrate using a cw Nd:YAG laser and a coaxial powder injection system. Coatings were made of an Al/Si matrix (powder size of 45–90 μm) containing 12 wt-%Si and reinforcements of WC (spherical or crushed), TiC or SiC particles (size of 50–150 μm) with a volume fraction ranging from 0 to 50%. Samples were characterised using optical microscopy, 278 hardness measurement and adhesive wear testing (ball–on-disk device). Reinforcement ratio increased the bulk coating hardness up to a maximum value of 280 HV5 regardless of the reinforcement particle nature. As the volume fraction of TiC, WC or SiC increases, adhesive wear damage evolved from severe to mild wear and the worn volume can be reduced by a factor of 20. Regardless of the reinforcement particle type, the higher wear resistance was observed with a reinforcement ratio of 35 vol.-%. Although the mechanical characteristics of the different reinforcement particles are dissimilar, no significant difference could be observed between laser cladded MMC coatings containing TiC, SiC or WC.
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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".