Microscopic Strain Mapping Using Scanning Electron Microscopy Topography Image Correlation at Large Strain
Bibliographic record
Abstract
Measuring the distribution of local strain at the microscopic level is a challenging problem, especially for materials subjected to large overall strain. In the present study, a novel microscopic strain mapping technique has been developed based on the analysis of surface topography using digital image correlation (DIC) software. The input is a series of scanning electron microscopy (SEM) images. The method uses topographic features (such as surface slip traces) found in these images as the input. A commercially available optical strain measurement system (ARAMIS®, which is a trade name of the equipment from GOM mbH, Braunschweig, Germany) that utilizes the DIC methodology is used for this purpose. It was found that the best results were obtained using an incremental approach in which DIC is used to map the local strain increments following a modest amount of macroscopic deformation. This is essential when using topographic features such as slip traces that are not static. The accuracy and scale of the measurements are affected by image and facet size. The method has been validated, based on in situ deformation of an aluminium alloy within an SEM, using strains measured independently by means of surface indents. The results clearly reveal the details of the local shear on a sub-grain-size scale and the evolution of shear bands within the necking area, leading to local strains that exceed the average strain by a factor of 2.3.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".