Evaluation of residual stress in thick metallic coatings using the combination of hole drilling and micro-indentation methods
Bibliographic record
Abstract
Plasma spraying is commonly used to deposit thick metallic coatings. The high deposition temperature and complex interaction of the process parameters result in tensile residual stresses in thick metallic layers. Tensile residual stresses are widely known to affect the integrity of metallic coatings. The present study uses a combination of hole drilling and micro-indentation techniques to evaluate the residual stress developed in Ni-based metallic coatings deposited on stainless steel substrate using a direct current plasma spray torch. The metallic coating samples are first characterized by microscopy, surface roughness measurement, micro-indentation, and scratch tests before through-thickness residual strain measurement via the incremental hole-drilling method. The residual stress in the metallic coating layers is evaluated from the incremental strain measurements and micro-indentation curves. The studies show that the residual stress can be reliably predicted using the combination of hole drilling and micro-indentation measurements. It is found that tensile residual stresses are developed across the depth of both NiCrAl and Ni–20Al coatings. The variation of the tensile residual stresses across the depth is nonlinear and almost equibiaxial. The residual stress strongly influences the adhesion strength of the thick metallic coating layers.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".