The application of digital image techniques to determine the large stress–strain behaviors of soft materials
Bibliographic record
Abstract
Abstract Understanding the mechanical properties of soft materials such as stress–strain behavior over a large deformation domain is essential for both mechanical and biological applications. Conventional measurement methods have limited access to these properties because of the difficulties in accurately measuring large deformations of soft materials. In this study, we optimized digital image correlation (DIC) method to measure the large‐strain deformations by considering referencing scheme and frame rate. The optimized DIC was utilized to estimate strain in characterizing the stress–strain behavior of a polydimethylsiloxane (PDMS) elastomer as a model soft material. A series of comparative experimental studies and finite element analysis were performed; they indicated the advantages of optimized DIC over conventional methods such as robustness to slip, insensitivity to boundary conditions, and the ability to yield consistent and reliable results. These advantages enabled the optimized DIC to perform an in‐depth analysis of the behavior of soft materials at large strain domain. An empirical constitutive equation to describe the large stress–strain behavior of PDMS was proposed and verified by finite element simulations that show excellent agreements with experimental results. POLYM. ENG. SCI., 2011. © 2011 Society of Plastics Engineers
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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 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".