Determination of Fracture Behavior of AA6060 Aluminum Alloy Extrusion Using Digital Image Correlation3© Her Majesty the Queen in Right of Canada, as represented by the Minister of Natural Resources, 2015.
Bibliographic record
Abstract
The dependence of fracture strain on stress triaxiality has been recently recognized as an important factor that controls the fracture of aluminum alloys. A number of experimental programs have been reported to determine fracture strains in a wide range of stress triaxiality using a variety of types of specimens. However, because of the lack of direct measurement of local strains near the fracture zone, indirect estimations of fracture strain are commonly used. The errors in determining fracture strain are uncertain. In this study we use the digital image correlation (DIC) method to determine the fracture strains in AA6060 aluminum extrusion material. This material is often used in automotive crash management systems. A commercially available DIC system was used to follow the deformation occurring during the tests of a set of newly designed specimens with a wide range of stress triaxiality; thus, the inception of instability and fracture can be captured and distinguished precisely. More importantly, post-experiment analysis in DIC allows strain calculations at macroscopic levels at varying step sizes, thus, the dependence of fracture strain on gauge length has been determined in each testing condition. The fracture locus of AA6060 aluminum extrusion has been successfully determined and the concept of “scaled fracture strain” has then been proposed to ensure consistency of the fracture locus in both the experiment and in modeling.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.001 |
| 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) | 0.003 | 0.001 |
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".