Experimental investigation on post-processed NiCr thermal barrier coating and its sliding wear behaviour
Bibliographic record
Abstract
NiCr metal is deposited on martensitic stainless steel using an atmospheric plasma spray method. To enrich the metallurgical properties of the NiCr metallic coating, a standard heat treatment process is adopted. From the investigation, it has been shown that the voids and porosity developed during the thermal spray have been controlled through heat treatment. Electron image analysis reveals that the coating has reduced porosity with strong and dense bonding strength. With reference to the surface hardness, the post-processed coating yields a maximum of 140 Hv compared with the coated (129 Hv) and base metal (115 Hv). Subsequently, the sliding wear behaviour of post-processed NiCr coating has a minimum wear of 25 μm for an applied load of 5 N and 57 μm for an applied load of 15 N. Owing to the high metallurgical bonding, the coating has sustained the heavy loads, and the wear formation is controlled. However, the base metal has an adhesive wear mechanism caused by the high frictional force on the sliding friction. Therefore, it is recommended that post-processed thermal barrier coatings possess good metallurgical bonding and withstand heavy load causing minimum wear.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.002 | 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".