Wear of D2 Tool Steel Dies during Trimming DP980-type Advanced High Strength Steel (AHSS) for Automotive Parts
Bibliographic record
Abstract
Automobile body panels made from advanced high strength steel (AHSS) provide high strength-to-mass ratio and thus AHSS are important for automotive light-weighting strategy. However, in order to increase their use, the significant wear damage that AHSS sheets cause to the trim dies should be reduced. The wear of dies has undesirable consequences including deterioration of trimmed parts' edges. In this research, die wear measurement techniques that consisted of white-light optical interferometry methods supported by large depth-of-field optical microscopy were developed. 1.4 mm-thick DP980-type AHSS sheets were trimmed using dies made from AISI D2 steel. A clearance of 10% of the thickness of the sheets was maintained between the upper and lower dies. The wear of the upper and lower dies was evaluated and material abrasion and chipping were identified as the main damage features at the trim edges. This paper quantifies the wear on the upper and lower dies between 20,000 - 230,000 trimming cycles and notes the observed differences between the wear characteristics in the upper and lower dies.
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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 | 0.000 |
| 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".