Evaluation of wear-induced plastic deformation at the trimmed edge of DP980 steel sheets
Bibliographic record
Abstract
In order to increase the use of advanced high strength steel (AHSS) for automotive light-weighting, the significant trimming-induced wear damage that AHSS sheets cause to the trim dies should be reduced so as to decrease die maintenance costs and deterioration of the quality of sheared edge. In this research, the wear characteristics in the upper AISI D2 die inserts used for trimming DP980-type AHSS sheets and the wear-induced plastic deformation at the sheared edges of DP980 were studied. Observation of wear profiles at the edge of the upper trim die revealed that material abrasion was the main damage feature. The trimmed edge quality of DP980 deteriorated with the number of strokes, as indicated by an increase in burr width. Plastic strains near the surface of the sheared edge estimated using the displacements of martensite plates, increased from 2.9 at 2.5 μm below the trimmed edge after 40,000 th cycles, to 55 after 80,000 th cycles. Micro-hardness tests performed to estimate the local flow stresses in the shear affected zone (SAZ) indicated that the flow stress at a distance of 4.5 μm below the trimmed edge increased with the number of trimming cycles to 1.73 GPa in the 80,000 th trimmed part.
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.
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 | 0.000 |
| 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) | 0.001 | 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".