Acoustic Microscopy of Internal Structure of Resistance Spot Welds
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| 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 it