Measurement of stress–strain behaviour of human hair fibres using optical techniques
Bibliographic record
Abstract
Many studies have presented stress-strain relationship of human hair, but most of them have been based on an engineering stress-strain curve, which is not a true representation of stress-strain behaviour. In this study, a more accurate 'true' stress-strain curve of human hair was determined by applying optical techniques to the images of the hair deformed under tension. This was achieved by applying digital image cross-correlation (DIC) to 10× magnified images of hair fibres taken under increasing tension to estimate the strain increments. True strain was calculated by summation of the strain increments according to the theoretical definition of 'true' strain. The variation in diameter with the increase in longitudinal elongation was also measured from the 40× magnified images to estimate the Poisson's ratio and true stress. By combining the true strain and the true stress, a true stress-strain curve could be determined, which demonstrated much higher stress values than the conventional engineering stress-strain curve at the same degree of deformation. Four regions were identified in the true stress-strain relationship and empirical constitutive equations were proposed for each region. Theoretical analysis on the necking condition using the constitutive equations provided the insight into the failure mechanism of human hair. This analysis indicated that local thinning caused by necking does not occur in the hair fibres, but, rather, relatively uniform deformation takes place until final failure (fracture) eventually occurs.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".