Application of non-associated flow rule for prediction of nonuniform material flow during deep drawing of tailor welded blanks
Why this work is in the frame
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Bibliographic record
Abstract
In order to enhance FE prediction capability, researchers are presently showing interest in applications of non-associated flow rule (NAFR) coupled with Hill48 quadratic function in different sheet metal forming operations. In this work, the concept of NAFR based model was implemented for the first time in FE simulation of deep drawing of DP980-IFHS tailor welded blanks (TWBs) of coated and uncoated sheets. The NAFR model was formulated using two approaches: namely, stress-value based Hill48 as yield function and R-value based Hill48 as the plastic potential function in the first approach, and the vice-versa in the second approach. Also, the classical associated flow rule (AFR) based approach coupled with the anisotropic Hill48 yield model was implemented in the FE simulation for comparison purpose. For improving the prediction accuracy, a mixed hardening equation by combining Voce and Swift hardening law was incorporated as the constitutive equation. It was found that FE simulation implementing the NAFR approach predicted the deep drawing behaviour of the parent materials and TWBs more accurately compared to that of the AFR approach.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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 it