Modelling the effect of temperature on the probabilistic stress–life fatigue diagram of glass fibre–polymer composites loaded in tension along the fibre direction
Bibliographic record
Abstract
Predicting the fatigue performance of composites has proven to be a challenge both conceptually, due to the inherent complexity of the phenomenon, and practically, because of the resource-intensive process of fatigue testing. Moreover, mechanical behaviour of polymer matrix composites exhibits a complicated temperature dependence, making the prediction of fatigue performance under different temperatures even more complex and resource intensive. The objective of this paper is to provide a method for the prediction of fatigue life of glass–polymer composites loaded in the fibre direction at various temperatures with minimal experimental efforts. This is achieved by using a static strength degradation approach to fatigue modelling, where only two parameters (including static strength) are temperature dependent, in conjunction with relationships for these two fatigue model parameters temperature dependence. The method relies on fatigue data at a single temperature and simple static tests at different temperatures to predict the effects of temperature on the material’s fatigue behaviour. The model is validated on experimental data for two unidirectional and one woven glass–epoxy composites and is found to accurately predict the effect of temperature on fatigue life of composites. A method to obtain probabilistic stress-life [Formula: see text] fatigue diagrams including temperature effects is also presented.
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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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".