Characterization of Lubricated Friction Behavior of Thermal Spray Steel Coatings in Comparison with Grey Cast Iron
Bibliographic record
Abstract
This work examines friction properties of smooth-honed thermal spray (TS) low carbon steel coatings produced on an Al-9.0% Si alloy using a plasma transferred wire arc (PTWA) method and an AISI 1010 wire used as feedstock in comparison with the ASM type D grey cast iron (CI) samples subjected to the same (smooth) honing process. CI samples prepared using a standard honing process were also tested for comparison. Reciprocating sliding tests were performed using a Cameron–Plint tribometer against CrN-coated counterfaces within a speed range of 0.06–1.20 m/s covering the boundary and mixed lubrication conditions. Stribeck curves were constructed to show the coefficient of friction (COF) variations with the ratio (λ) of lubricant film thickness to composite surface roughness of TS and CI samples at the mid-stroke position where sliding speeds and surface roughnesses were measured. Examination of the Stribeck curves showed that the TS coated surfaces provided lower COF values compared to CI surfaces given the same smooth honing treatment, e.g., for λ = 2.7 a COF of 0.029 was observed for TS and 0.035 for CI, whereas conventional honing of CI provided a COF of 0.047 under the same condition. Metallographic evidence was given for the surface features and formation of tribolayers on the contact surfaces. The arithmetic mean heights of the surfaces, Sa measured after the tests remained similar for the smooth-honed TS and CI samples. The low COF values of the TS samples were discussed in terms of the surface pores generated during their manufacturing process, and the high oil retention depth ratio (Svk/Sk) of the TS coated surfaces due to the presence of these pores.
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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".