Optimization of Test Parameters for Magneto-Optic Imaging Using Taguchi's Parameter Design and Response-Model Approach
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
Magneto-optic/eddy current imaging (MOI) is becoming widely used for aging aircraft inspection for cracks and corrosion. However, many test parameters affect the accept/reject decision about a test sample and hence the overall performance of MOI system. The optimization of the parameters is extremely crucial in enhancing the performance of MOI system. This article uses the Taguchi method to change parameter values simultaneously to search for the optimum set of test parameters for maximizing system performance for a given sample geometry and critical crack. It is also important at the same time the system performance be unaffected by variations in parameters. Efficiency of Taguchi's partial factorial design is obvious. The optimum set of parameters is found by means of analyses of main effects. Analysis of variance identifies those parameters that need to be controlled carefully. A response-model approach is utilized as a complement to the Taguchi method.
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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 | 0.007 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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