On the use of cyclic shear, bending and uniaxial tension–compression tests to reproduce the cyclic response of sheet metals
Bibliographic record
Abstract
Simple shear, uniaxial tension–compression and bending tests were used to determine the cyclic behaviour of two sheet metals: DP600 and AKDQ. The Yoshida–Uemori two-surface model along with Hill’s quadratic yield function was used to simulate the behaviour of these two materials in each test. For each test, a set of material constants was identified such that the error between the simulated and experimental responses is minimized. Using the material constants obtained from one test, the other tests were simulated to see whether the set of constants obtained from this test is able to describe the material response in the other tests. The results show that depending on the material, the set of constants obtained from one test may or may not be able to reproduce the material response in the other tests. Finally, each set of constants was used to simulate the springback of a U-shaped part formed in a channel draw process. The predicted springback profiles obtained from each set of constants were compared with the experimental profile. It was found that all three tests are suitable to characterize the behaviour of DP600 sheets in view of predicting the springback of channel sections. For AKDQ, however, the error between the predicted and experimental springback profiles was significant regardless of the type of characterization test performed. But for this channel draw process, simulations based on material data obtained from the reverse bending test provided the best prediction of springback.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".