Surface Fatigue Cracking Behavior of a CrN-Coated Tool Steel Influenced by Sliding Cycles and Sliding Energy Density
Bibliographic record
Abstract
Light-weighting of vehicles is one of the challenges for transportation industry due to the increasing pressure of demands in better fuel economy and environment protection. Advanced high strength steels (AHSS) are considered as prominent material of choice to realize lightweight auto body and structures at least in near term. Stamping of AHSS with conventional die materials and surface coatings, however, results in frequent die failures and undesired panel surface finish. A chromium nitride (CrN) coating with plasma nitriding case hardened layer on a die material (duplex treatment) is found to offer good wear and galling resistances. The coating failure initiates from fatigue cracking on the coating surface due to cyclic sliding frictions. In this work, cyclic inclined sliding wear test was used to imitate a stamping process for study on development of coating fatigue cracking, including crack length and spacing vs. sliding-cycles and sliding energy densities. The purpose of this study was to examine the effect of testing cycles and sliding energy work on evolution of coating fatigue cracking. In addition, the proportion of total energy transferring to surface energy as a path of energy dissipation through generation of new surfaces of cracks was analyzed. The result indicated that such an energy conversion was insignificant; suggesting that the computing simulation output should also include information of stresses on the die surface and more attention should be paid on response of the coated die material to the stresses.
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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) | 0.000 | 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".