Induced defect layer method to characterize the effect of fiber tow gaps for the laminates manufactured by automated fiber placement technique
Bibliographic record
Abstract
Automated manufacturing defects are new types of composite structure defects induced during fiber deposition by robots. Fiber tow gap is one of the most probable types of defects observed in the Automated Fiber Placement (AFP) technique. This defect can affect the structural integrity of structures by reducing structural strength and stiffness. The effect of this defect on the mechanical response of the composite laminates has been investigated experimentally in the literature. However, there is still no efficient numerical/analytical method for damage assessment of composite structures with distributed induced gaps manufactured by the AFP technique. The present paper aims to develop the Induced Defect Layer Method (IDLM), a new robust meso-macro model for damage analysis of the composite laminates with gaps. In this method, a geometrical parameter, Gap Percentage (GP), is implemented to incorporate the effect of induced-gaps in the elastic, inelastic, and softening behavior at the material points. Thus, while the plasticity and failure of the resin pockets in conjunction with intralaminar composite damages can be evaluated by this method, the defective areas are not required to be defined as resin elements in the Finite Element (FE) models. It can also be applied for any arbitrary distributions of the defects in the multi-layer composite structures, making it a powerful tool for continuum damage analysis of large composite structures. Results indicate that the proposed method can consider the effect of gaps in both elastic and inelastic behavior of the composite laminate with defects. It also provides good agreement with the experimental results.
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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) | 0.001 | 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 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".