Evaluation of residual stress in thick metallic coatings using the combination of hole drilling and micro-indentation methods
Why this work is in the frame
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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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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 | 0.013 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 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) | 0.000 | 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