Phased array ultrasonic imaging and characterization of adhesive bonding between thermoplastic composites aided by machine learning
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
The testing and evaluation of adhesive bonding quality between thermoplastics are crucial for structural integrity. This article presents the use of phased array ultrasonic testing (PAUT) method to characterise the adhesive interface between thermoplastic composites. Samples with three different bond conditions: control, bad and mid-level were fabricated and tested using PAUT. A damage index (DI) based classification framework aided by machine learning (ML) algorithm is proposed to classify different adhesion conditions. A set of 18 physics-based damage indices were extracted from each PAUT image for quantitative characterisation. ML algorithms were developed to build a non-linear mapping that correlates the input DIs with the output sample types to address the classification problem. The experimental results show that support vector machine (SVM) performs better than other ML algorithms with classification accuracy greater than 95%, and the defined DIs can differentiate among bad, mid-level, and control samples.
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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 | 0.001 | 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