Prediction of Off-Axis Nonlinear Material Behavior for Unidirectional CFRTP Using Numerical Material Tests
Bibliographic record
Abstract
This study aimed to examine the nonlinear material behavior of unidirectional carbon fiber reinforced thermoplastics (UD-CFRTP) under off-axis loading using numerical material tests (NMTs). A method to identify the appropriate material properties of an assumed macroscopic anisotropic constitutive law is proposed. To identify the macroscopic material properties using an optimization method, seven macroscopic deformation modes (three vertical directions, three shear directions, and a deformation pattern in the off-axis direction of 45º) were studied for preparing the virtual material responses. Identification accuracy was verified by comparing the predicted macroscopic material responses with those obtained using the actual off-axis tensile test. The extensive use of the NMTs with seven macroscopic deformation modes enabled the successful confirmation of the material properties of the assumed macroscopic constitutive law for UD-CFRTP. This was identified by comparing the NMT results obtained using the off-axis macroscopic deformation and standard six modes with those obtained using only six modes.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| 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) | 0.001 | 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 source (direct Gemma or distilled Codex), 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".