Evaluation of Interface Adhesion Strength of Unidirectional CFRP Using Numerical Material Test and Neural Network
Bibliographic record
Abstract
When analyzing the fracture behavior of unidirectional carbon fiber-reinforced polymer (CFRP), it is important to consider the interfacial strength between the reinforcing fiber and the base resin, and the strength of the base resin. Therefore, the adhesiveness of the base material and the compatibility with the sizing material and fibers are important design parameters in the development of CFRPs. However, a quantitative method for estimating the interfacial strength and the strength of the base resin has not been established. In this study, we propose a method to evaluate the interface strength of unidirectional CFRPs by creating learning data through a series of numerical material tests and by constructing a neural network that outputs the interface strength based on a homogenization method from the results of off-axis tensile tests. We adopt a general feed forward neural network whereby parameters are learned by employing a backpropagation method. The interfacial strength and the matrix resin strength is predicted and evaluated from the results of the off-axis tensile test to demonstrate the effectiveness of this system.
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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 | 0.002 | 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 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".