Mechanical property changes in HVOF sprayed nano-structured WC-17wt.%Ni(80/20)Cr coating with varying substrate roughness
Why this work is in the frame
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Bibliographic record
Abstract
Thermally sprayed coatings developed by use of high velocity oxy-fuel (HVOF) process are known for their superior wear characteristics. In many industrial applications, new parts as well as repaired and refurbished parts coated with WC-Co microstructured coatings have shown enhanced erosion-corrosion and abrasive resistant properties when compared with other surface modification technologies such as chrome replacement, fusion welding, and cladding. This research has been further directed towards the development of HVOF technique to deposit dense nanostructured ceramic-metallic composites. The mechanism of plastic deformation, which determines the strength and ductility of materials, in nanostructured materials are different, thereby leading to novel mechanical properties. Various parameters can influence these properties, but the substrate surface preparation by grit blasting before thermal spraying is one critical parameter. The grit blasting process generates a surface roughness, which ensures mechanical anchoring between the coating and the substrate surface. In this work, the sliding wear behavior and microhardness of WC-17wt.%Ni(80/20)Cr cermet coatings deposited onto carbon steel substrates are examined as a function of three different surface roughness values under different loads. The results show that as-prepared surface with different blasting profiles have a direct influence on the surface roughness and wear performance of the coatings. The sliding wear resistance of the coatings increased as the substrate surface roughness increased. The wear depth decreased with increasing surface roughness.
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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 | 0.001 | 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 | 0.001 | 0.001 |
| 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 it