Hybrid Co-Cr/W-WC and Ni-W-Cr-B/W-WC Coating Systems
Bibliographic record
Abstract
Abstract The mechanical, tribological, and corrosion properties of two hybrid coating systems were assessed: 1) a tungsten–tungsten carbide (W-WC) top layer and a laser cladded cobalt– chromium (Co-Cr) interlayer (Stellite 6 superalloy) applied to a 316 stainless steel substrate; and 2) the same W-WC top layer and an HVOF spray-and-fused Ni-W-Cr-B interlayer (Colmonoy 88 superalloy) applied to an Inconel 718 substrate. X-ray diffraction, energy dispersive spectroscopy, and scanning electron microscopy were used to analyze the microstructure of the coating layers. Microindentation was used to measure surface hardness and the hardness profile of the coating systems. Rockwell indentation was used to assess coating adhesion according to CEN/TS 1071-8. Surface load-carrying capacity was also assessed by measuring micro- and macrohardness at high loads. Tribological properties were assessed with a linear reciprocating ball-on-flat sliding wear test, and corrosion resistance was measured by potentiodynamic polarization and electrochemical impedance spectroscopy. W-WC layers showed class I adhesion to both the SS 316 and Inconel 718 substrates, with and without an interlayer. Hardness profile measurements on cross section showed hardness of 13.6 GPa and 7.0 GPa for W-WC and Co-Cr, respectively, with average hardness of 9.7 GPa for Ni-W-Cr-B. Furthermore, hardness measurements at different high loads revealed that the addition of an interlayer increases surface hardness by up to 200% compared to the same coating system provided without an interlayer, quantifying the additional load-carrying capacity provided by the supplementary interlayer. The tribological measurements show that, except for the Inconel 718 / Ni-W-Cr-B / W-WC system, the hardest interlayer or substrate leads to the highest wear rates. In addition, the W-WC layer showed excellent corrosion protection, with no pitting observed after potentiodynamic polarization testing.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".