Nondestructive Evaluation of Electroplating-Induced Hydrogen Embrittlement in Cadmium-Coated High-Strength Steel Using Ultrasonic Surface Waves
Bibliographic record
Abstract
Development of a nondestructive evaluation (NDE) method to detect nascent hydrogen embrittlement (HE) in electroplated high-strength steel parts is becoming important for the aerospace industry. This research investigates the feasibility of surface acoustic waves (SAWs) measurements to distinguish between cadmium (Cd) plated SAE 4340 steel samples with low and high HE susceptibilities. SAWs were generated with a 10 MHz piezoelectric transducer and detected by line scans via a laser Doppler vibrometer setup. Using signal processing algorithms in MATLAB, SAW velocities as well as attenuation coefficients were estimated. Depth profiles of steel hardness near coatings were also evaluated using Vickers microindentation tests. Average steel hardness in not-baked samples was slightly increased. Cd coatings were characterized by laser and optical microscopy methods. Small variations found in thickness and surface roughness of the Cd coatings among the samples did not significantly affect the NDE results. On average, samples in the not-baked condition (high HE risk) exhibited lower SAW attenuation coefficients compared to immediately baked and late-baked conditions (low HE risk). However, it was not possible to distinguish between the manufacturing conditions of individual samples due to overlaps in attenuation measurement results. SAW velocities as estimated by the cross-correlation method were found to be not sensitive to manufacturing conditions.
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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 | 0.001 |
| 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) | 0.000 | 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 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".