Investigation of creep and fatigue in high temperature polymer matrix composites using a micromechanical approach
Bibliographic record
Abstract
Polymer matrix composites (PMC’s) are widely used in critical aerospace structures due to their numerous advantageous mechanical properties. Recently, PMC’s have been considered for high temperature applications where viscoelasticity arising from the time dependent nature of the polymer matrix becomes an important consideration. This inherent viscoelasticity can significantly influence deformation, strength and failure response of these materials under different loading modes and environmental factors. With a potentially large number of plies of different fiber directions and perhaps material properties, determining a fatigue failure criterion of any degree of generality through experiments only, may seem to be an unrealistic task. This difficult situation may be mitigated through the development of suitable theoretical micro or macro mechanical models that are founded on considering the fatigue failure of the constituting laminas. The micro‐approach provides a detailed examination of the individual failure modes in each of the constituent materials i.e. fiber, matrix. In this work, a micromechanical approach is used to study the role of viscoelasticity on the fatigue behavior of polymer matrix composites. In particular, the study examines the interaction of fatigue and creep in polymer matrix composites. The matrix phase is modeled as a vicoelastic material using Schapery’s single integral constitutive equation. Taking viscoelsticity into account allows the study of creep strain evolution during the fatigue loading. The fatigue failure criterion is expressed in terms of the fatigue failure functions of the constituent materials. The micromechanical model is also used to calculate these fatigue failure functions from the knowledge of the S‐N diagrams of the composite material in longitudinal, transverse and shear loadings thus eliminating the need for any further experimentation. Unlike the previous works, the present study can distinguish between the strain evolution due to fatigue and creep. The results can clearly show the contribution made by the effect of viscoelasticity to the total strain evolution during the fatigue life of the specimen. Although the effect of viscoelsticity is found to increase with temperature, its contribution to strain development during fatigue is compromised by the shorter life of the specimen when compared to lower temperatures.
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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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".