Measuring the Mechanical Properties of Aluminum Sheets and Their Resistance Spot Welds at Large Strains Using Digital Image Correlation Coupled with a Modified Shear Test
Bibliographic record
Abstract
The constitutive behavior of aluminum alloy sheet and their resistance spot welds at large strains is critical for light weight vehicle design analysis and life prediction. However, data from uniaxial tensile tests are usually limited to small strains or by material instability. A novel technique was developed using digital image correlation coupled with a modified shear test to directly measure the stress - strain curves of aluminum alloy sheet at large strains. The modified shear sample prevents end rotation of the shear zone as compared to the ASTM B831 test. The results show that the effective stress - effective strain curves from shear tests match those obtained by uniaxial tension, but only by incorporating material anisotropy using the Barlat-Lian yield function. For the first time, the technique was applied to aluminum resistance spot welds to determine both the shear strength and stress-strain curves of spot welds at large strains. The results show that for non heat treatable AA5754 spot welds, there was a slight change in yield strength but significant change in work hardening rate whereas for heat treatable AA6022-T4 spot welds, there was a significant decrease in both yield strength and work hardening rate. Technique details and experimental validation will be discussed.
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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 | 0.001 | 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) | 0.002 | 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".