Elastic Modulus Measurements via Laser-Ultrasonic and Knoop Indentation Techniques
Bibliographic record
Abstract
Abstract Nondestructive techniques for evaluating and characterizing coatings have been extensively demanded by the thermal spray community; nonetheless, few results have been produced in practice due to difficulties in analyzing the complex structure of thermal spray coatings. Of particular interest is knowledge of the elastic modulus values and Poisson’s ratios, which are very important when seeking to understand and/or model the mechanical behavior or develop life prediction models of thermal spray coatings employed in various applications (e.g., wear, fatigue and high temperatures (TBCs)). In the present study, two techniques, laser-ultrasonics and Knoop indentation, were used to determine the elastic modulus of thermal spray coatings. Laser ultrasonics is a non-contact and nondestructive evaluation method that uses lasers to generate and detect ultrasound. Ultrasonic velocities in a material are directly related to its elastic modulus value. The Knoop indentation technique, which has been widely used as a method for determining elastic modulus values, was employed in order to compare and validate the measurements of the laser-ultrasonic technique. The determination of elastic modulus values via the Knoop indentation technique is based on the measurement of elastic recovery of the dimensions of the Knoop indentation impression. The approach used in the present study was to focus on evaluating the elastic modulus of very uniform, dense and near-isotropic titania and WC-Co thermal spray coatings using these two techniques. Four different coatings were evaluated: two titania coatings produced by APS and HVOF and two types of WC-Co coatings, conventional and multimodal (nanostructured and micro-sized particles), deposited by HVOF.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".