Probabilistic first ply failure prediction of composite laminates using a multi-scale M-SaF and Bayesian inference approach
Bibliographic record
Abstract
This paper presents a probabilistic first ply failure analysis of composite laminates using a high-fidelity multi-scale approach called M-SaF (Micromechanics-based approach for Static Failure). To this end, square and hexagonal representative unit cells of composites are developed to calculate constituent stresses with the help of a bridging matrix between macro and micro stresses referred to as the stress amplification factor matrix. Separate failure criteria are applied to each of the constituents (fiber, matrix, and interface) in order to calculate the damage state. The successful implementation of M-SaF requires strength properties of the constituents which are the most difficult and expensive to characterize experimentally, limiting the use of M-SaF in the early design stages of a structure. This obstacle is overcome by integrating a Bayesian inference approach with M-SaF. An academic sample problem of a cantilever beam is used to first demonstrate the calibration procedure. Bayesian inference calibrates the M-SaF first ply failure model parameters as posterior distributions from the prior probability density functions drawn from lamina test data. The posterior statistics were then used to calculate probabilistic first ply failure for a range of different laminates.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".