Revealing the Evolution in Microstructure and Mechanical Properties of Cam Tappet After Surface Texture Combination Nitriding Treatment Under Different Process Sequences
Bibliographic record
Abstract
Severe wear of the tappet surface in the engine cam‐tappet friction pair under heavy load and high‐speed conditions reduces the vehicle's service life and accelerates the emission of pollutant gases. In this study, laser surface texturing and ion nitriding are employed to modify the surface of GCr15 steel through a double treatment process. The effect of the process sequence is rarely discussed in previous research and thus particularly focuses on the changes in tappet properties resulting from different process sequences. The results reveal that the wear resistance of the tappet improves to varying extents after surface modification. It is also observed that the friction coefficient of the tappet is highly sensitive to the sequence of processes. This is attributed to the fact that nitriding improves the hardness of the microconvex structures formed during the texturing, which leads to higher contact stresses during friction, thereby compromising the integrity of the textured. Additionally, nitriding enhances the tappet's corrosion resistance. Therefore, considering the sequence of processes in the Combined modification of GCr15 steel of the tappet, it provides valuable data for the design and optimization of tappets.
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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".