A Novel Estimation of Tearing Limit in Deep Drawing Process Based on Strain Energy; Experimental Characterization and Numerical Validation
Bibliographic record
Abstract
Abstract Deep drawing is a forming process widely used in aerospace, military, automotive, and various industries. One of the essential useful parameters in the quality of deep-drawn products is Blank Holder Force (BHF). By Controlling BHF during the process, formability has been improved, reduced forming energy, and sheet thickness. Tearing is one of the most common and crucial defects in this process due to high radial stress in the cup's wall, resulting in many limitations in this field. In this process, the blank holder force plays an indispensable role in causing tearing. Therefore, controlling blank holder force during the process would be inevitable to avoid tearing or even wrinkling. This study aims to calculate the tearing limit with new criteria in analytical dominating plasticity Equations based on the slab Method. The St14 sheet with 1 mm thickness and 200 mm diameter is used in this study. The maximum blank holder force in each stage of punch stroke with new criteria based on strain energy with three different frictional coefficients is calculated, compared, and verified with FEM simulation and experimental results. Analytical results with new criteria are also compared with previous procedures. Finally, it is realized that results related to energy criteria have a lot more similarity with FEM simulation and experimental results in comparison with the previous ones.
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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 | 0.001 |
| 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 | 0.001 |
| 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".