Wear Resistance of Stellite-6/TiC Coating on Stainless Steel 316L Produced by Laser Cladding Process
Bibliographic record
Abstract
Austenite stainless steel materials find vast applications in nuclear power plants due to their excellent corrosion resistance, but they have relatively poor wear resistance due to their low hardness.The wear resistance of these materials can be improved by modifying surface characteristics, which is achieved by adopting different coating techniques.This paper study the wear resistance of stellite-6/TiC (Titanium Carbide) coated on a Stainless Steel 316L (SS316L) base material prepared from laser cladding technique.The samples are cladded with particles of stellite-6 and a reinforcement coating with TiC to base material to improve wear resistance.Experiments are carried out with varying Titanium carbide percentages of 0, 10, 20, 30, 40 and 50 with respect to stellite-6 composition which has been varied up to 100%.Wear test is carried out by using Pin-on-Disc method at room temperature.The entire study has been carried out at a cladding thickness of 1.6mm.The micro-structural behavior of wear samples has been captured using Scanning Electron Microscope (SEM) and EDAX spectra.The results show that, stellite-6 with TiC 10% and 20% coating is more effective than other compositions to improve the wear resistance.
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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".