Application of non-associated flow rule for prediction of nonuniform material flow during deep drawing of tailor welded blanks
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.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".