Prediction of Fracture Strains for DP980 Steel Sheets Using a Modified Lou–Huh Ductile Fracture 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
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.
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.
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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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 it