Prediction of Fracture Strains for DP980 Steel Sheets Using a Modified Lou–Huh Ductile Fracture Criterion
Bibliographic record
Abstract
This paper is concerned with the prediction of fracture strains for DP980 steel sheets using a modified Lou–Huh ductile fracture criterion. The usage of DP980 steel is significantly increasing in the automotive industry for weight reduction, enhancement of crashworthiness and safety of car body. The material behavior of AHSS show unpredictable and sudden fracture during sheet metal forming process. A modified Lou–Huh ductile fracture criterion is utilized to predict the formability of AHSS because the conventional FLD constructed based on necking is unable to evaluate the formability of AHSS. Fracture loci were extracted from 3D fracture envelopes by assuming the plane stress condition to evaluate equivalent plastic strain up to the point of fracture at a wide range of loading paths. Three different types of specimens such as pure shear, dog-bone and plane strain grooved specimens were used for tensile tests to construct 3D fracture envelopes of DP980. Fracture strain of each loading path was evaluated to show that there is little deviation between predicted fracture strains and experimentally acquired ones. From the comparison, it is concluded that the 3D fracture envelopes can accurately predict the onset of the fracture of DP980 steel sheets in complicated loading conditions including the pure shear condition.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".