Experimental determination of strain in thin aluminum plate with central hole subjected to far-field tensile loading using digital image correlation (DIC)
Bibliographic record
Abstract
A flat Aluminum specimen with a geometric discontinuity, which allows testing of the applicability of 2D and 3D Digital Image Correlation Strain measurements, has been considered for this research since it is prone to high stress concentration via Addis Ababa Institute of Technology research interest. Experimental strain using digital image correlation and geometry measurements should be measured with estimated material properties and compare the results with theoretical model predictions. Aluminum plate with central hole were subjected for far field stress in the Machine shop of School of Mechanical and Industrial Engineering at Addis Ababa University in order to test the agreement between DIC’s strain analysis, strain gauge strain analysis and calculated empirical formulas of strain analysis and for the stress distribution of the plate elastically deformed by using VIC-3D and strain gauge. The aim is to measure vertical strain field, Vertical strain along horizontal line through hole center as function of applied loading and vertical strain using theoretical formula along same line as measurements in a plate with central hole subjected to far field and near field tensile loading using visual image coloration (VIC-3D) software, Destensometere device and solid mechanics equations to compare strain results.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".