Tribo Mechanical Properties of CoCr and NiWCrB Hardfacing Superalloy Coating Systems
Bibliographic record
Abstract
Wear of materials is a serious problem facing industry especially in mechanical applications where moving parts are continuously subjected to friction. Hard coatings prepared by a variety of processes are nowadays considered as effective solutions to protect components against wear. Examples of such processes are: thermal spray coating, vacuum-based coating and hardfacing. In this paper, we study the mechanical, tribological and corrosion properties of two hard coating systems: CoCr Stellite 6 (ST6) hardfacing on 316 stainless steel and NiWCrB Colmonoy 88 (C88) thermal spray coating on Inconel 718. The effect of gas nitriding on the microstructure and wear performance of these coating systems is investigated. X-ray diffraction, energy dispersive spectroscopy and scanning electron microscopy were used for microstructural analysis. Micro-indentation technique was utilized to measure the surface and cross-sectional hardness of the coatings. Rockwell indentation technique was used to evaluate coating adhesion in accordance with CEN/TS 1071-8. Pin-on-disk tests were conducted to assess the tribological performance of the coatings. Microstructural analysis showed that ST6 has a cobalt matrix in the form of dendrites reinforced with metal carbide particles whereas C88 has a Nickel matrix reinforced mainly with metal boride particles. ST6 and C88 improved significantly the wear resistance of their corresponding substrates. This is mainly due to good adhesion and high hardness of the coatings; HR15N values of ST6 and C88 were almost 85 as compared to 61 and 80 for 316 and INC substrates, respectively. ST6 was found to improve significantly the corrosion resistance of 316 whereas C88 decreased the corrosion performance of INC. Moreover, nitriding treatment was found to improve significantly the wear resistance of 316 and INC, however, in the case of ST6, nitriding was beneficial in terms of wear resistance only at relatively low load.
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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".