Prediction of Interfacial Adhesion Strength in CFRTP considering Plasticity of Matrix Resin using Numerical Material Testing and Neural Network
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Bibliographic record
Abstract
We propose a method for predicting the interfacial adhesion or bond strength of unidirectional carbon fiber reinforced thermoplastic plastics (UD-CFRTP) using a neural network (NN) and numerical material testing (NMT) that takes into account the plastic behavior of resin. In the proposed method, first, elastoplastic materials are assumed for the matrix resin, and macroscopic fracture strengths are calculated from NMTs that simulate off-axis tensile tests of UD-CFRTP. Next, a series of NMTs are performed by varying the interfacial adhesion strength between the fiber and resin, the fracture strength of the matrix resin, and the fiber volume fraction, respectively, and the relationships with the obtained macroscopic fracture strengths of UD-CFRTP are learned by the NN. Then, using the learned NNs, the microscopic interfacial adhesion strength and fracture strength of the matrix resin are predicted from the results of actual off-axis tensile tests of UDCFRTP. To verify the accuracy of the proposed method, NMTs are conducted using the predicted strengths, and the results are compared and evaluated with the results of actual off-axis tensile tests of UD-CFRTP.
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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 | 0.000 | 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