Constitutive modeling and experimental validation of the thermo-mechanical response of a shape memory composite containing shape memory alloy fibers and shape memory polymer matrix
Bibliographic record
Abstract
Multifunctional shape memory composites composed of shape memory polymers and shape memory alloys exhibit superior shape memory properties; therefore, it is of great interest to researchers to model their thermo-mechanical behavior numerically. Although a number of constitutive models of shape memory alloys and shape memory polymers have been developed, very few models have been developed for shape memory composites and validated with experimental data. In this study, we first review separately constitutive models of shape memory alloys and shape memory polymers developed in previous studies. Both models were validated with thermo-mechanical tests conducted on a shape memory alloy fiber and shape memory polymer, respectively. A constitutive model for the shape memory composite was then developed utilizing the homogenization scheme. Shape memory composites containing 0.5% or 1% of the shape memory alloy fiber volume content embedded in the shape memory polymer matrix were fabricated. Thermo-mechanical tests were carried out on these shape memory composites to validate the proposed constitutive model. The experimental results showed that the proposed shape memory composite model was able to predict the general trend of the thermo-mechanical behavior of the shape memory composites.
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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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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 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".