Acoustic Microscopy of Internal Structure of Resistance Spot Welds
Bibliographic record
Abstract
Acoustic microscopy, although relatively new, has many advantages within the industrial quality control process. Its high degree of sensitivity, resolution, and reliability make it ideal for use in resistance spot weld analysis, aiding in visualization of small-scale nugget failures, as well as other defects, at various depths. Acoustic microscopy makes it possible to inspect fine detail of internal structures, providing reliable inspection and characterization of weld joints. Besides weld size measurements, this technique is able to provide high resolution, three-dimensional images of the weld nuggets, revealing possible imperfections within its microstructure that may affect joint quality. The high degree of accuracy allows one to consider the results of acoustic microscopy an authoritative measure of weld size, particularly in the case of high strength steels, dual phase steel, USIBOR steel, etc. Indeed, this technique is effective even when both conventional ultrasound and hammer and chisel methods are not. In this paper, the potential of scanning acoustic microscopy as a means to provide qualitative and quantitative information about the internal microstructure of the resistance spot welds is demonstrated. Thus, acoustic microscopy is shown to be a unique and effective laboratory instrument for the evaluation and calibration of weld quality.
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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 | 0.001 | 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".