Analysis of stress distribution of CFRP bonded joints: A study of numerical and machine learning approach
Bibliographic record
Abstract
We aim to provide a study of material selection at the upper adherend subject for optimization of carbon fiber reinforced polymer (CFRP) adhesive-bonded joints with details on the stress distribution in the complex tri-material joint structure. The structure consists of a changing upper adhesive and CFRP lower adhesive, all bonded by a nano-thickness resin adhesive layer. The research, which analyzes almost 100 upper adherend substrates, hopes to answer how they influence stress distribution at the apex of a joint, a critical factor in bond strength. These results are essential in selecting the donor properties of the upper adherend in CFRP bonded joints. As this study also supports engineers and researchers in devising optimized machine learning models for addressing CFRP-bonded joint challenges, the accuracy of stress prediction is improved by applying machine learning techniques to the collected data more refinedly.
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How this classification was reachedexpand
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 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".