AISI/DOE Advanced Process Control Program Vol. 4 of 6: ON-LINE, NON-DESTRUCTIVE MECHANICAL PROPERTY MEASUREMENT USING LASER-ULTRASOUND
Bibliographic record
Abstract
The goal of this project was to demonstrate the feasibility to measure the mechanical properties, such as yield strength, tensile strength, elongation, strain hardening exponent and plastic strain ratio parameters, of low carbon steel sheets on the production line using laser ultrasound. The ultrasound generated by the developed apparatus travels mostly back and forth in the thickness of the steel sheet. By measuring the time delay between two echoes, and the relative amplitude of these two echoes, one can measure ultrasound velocity and attenuation. These are governed by the microstructure: grain size, crystallographic texture, dislocations, etc. Thus, by recording the time behavior of the ultrasonic signal, one can extract microstructural information. These microstructural information together with the modified Hall-Petch equation allow measurement of the mechanical properties. Through laboratory investigations with a laboratory laser ultrasound system, followed by the installation of a prototype system at LTV Steel Company's No.1 Inspection Line in Cleveland, all target mechanical properties of ultra low carbon (ULC), low carbon (LC) and high strength low alloy (HSLA) steel sample lots were measured meeting or nearly meeting all the target accuracies. Thus, the project realized its goal to demonstrate that the mechanical properties of low carbon steel sheets can be measured on-line using laser ultrasound
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".