In-Situ Characterization of Isotropic and Transversely Isotropic Elastic Properties Using Ultrasonic Wave Velocities
Bibliographic record
Abstract
Abstract In this paper, a one-sided, in situ method based on the time of flight measurement of ultrasonic waves was described. The primary application of this technique was to non-destructively measure the stiffness properties of isotropic and transversely isotropic materials. The method consists of generating and receiving quasi-longitudinal and quasi-shear waves at different through-thickness propagation angles. First, analytical equations were provided to calculate the ultrasonic wave velocities. Then, an inverse method based on non-linear least square technique was used to calculate the stiffness constants using the ultrasonic wave velocities. Sensitivity analysis was performed by randomly perturbing the velocity data, thus observing the effects of perturbations on the calculated stiffness constants. An improved algorithm was proposed and tested to reduce the effects of random errors. Based on the sensitivity analysis, minimum number of angles required to inversely calculate the stiffness constants were suggested for isotropic and transversely isotropic material. The method was experimentally verified on an isotropic 7050-T7451 aluminum with two different thicknesses and a transversely isotropic composite laminate fabricated using 24 plies of CYCOM 977-2 12 k HTA unidirectional carbon fiber reinforced polymer (CFRP) prepregs. The results demonstrated that this technique is able to accurately measure the material properties of isotropic material. As for the transversely isotropic material, this method was able to accurately measure the material properties if the experimental errors can be reduced to less than 1 %.
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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) | 0.001 | 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".