Parametric Shape Optimization of Stretch Webs in a Progressive Die Process using a Neural Network Surrogate Model
Bibliographic record
Abstract
Abstract Progressive die stamping provides a solution for producing sheet metal parts in large quantities. These parts are connected to carriers by stretch webs. As the part undergoes bending and forming operations, the stretch webs are exposed to translational and rotational deformation. A suitable design of these entities is crucial to avoid failure caused by splits or excessive thinning. A common way to evaluate such designs is to use finite element (FEM) simulation. Since it is not efficient to run FEM based optimization studies for the design optimization and to enable further automation of the stretch web design, this paper is proposing the use of machine learning (ML) technologies. A surrogate model based on an artificial neural network is used as a predictor in the presented study. This neural network is used to optimize the geometric parameters of the stretch web to obtain a quality result. The model is trained using FEM results and the study shows that it was possible to obtain an accurate model with a prediction error of 5%. The trained surrogate model can be used for the optimization study. This approach is computationally inexpensive and can provide very good results.
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".