Failure Prediction of Automotive Components Utilizing a Path Independent Forming Limit Criterion
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
An area in the automotive industry that receives a lot of attention today is the introduction of lighter and more advanced material grades in order to reduce carbon emissions, both during production and through reduced fuel consumption. As the complexity of the introduced materials and component geometries increases, so does the need for more complex failure prediction approaches. A proposed path-independent failure criterion, based on a transformation of the limit curve into an alternative evaluation space, is investigated. Initially, the yield criterion used for this transformation of the limit curve was investigated. Here it was determined that the criterion for the transformation could not be decoupled from the material model used for the simulation. Subsequently, the approach using the transformed limit curve was tested on an industrial case from Volvo Cars, but a successful failure prediction was not obtained.
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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) | 0.000 | 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 | 0.001 |
| 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