Effect of crescent-shaped texture based on different depths and orientations on the frictional properties of cam tappets
Bibliographic record
Abstract
The cam tappet friction pair is one of the three major friction pairs in an internal combustion engine, and the surface of the tappet undergoes severe wear during operation. To improve the wear resistance of the cam tappet friction pair during operation, in this study, crescent-shaped micro-textures were prepared on the surface of the tappet material of GCr15 steel by laser machining technique, and the effects of the orientation and depth of the texture on the friction properties of GCr15 steel were studied. The processed test samples were tested and characterized by friction and wear tests, scanning electron microscopy (SEM), 3D morphometry, and EDS. The flow field simulation model of micro-texture was established by using Fluent finite element software. The simulation and experimental results show that the crescent-shaped microtexture prepared on the surface of GCr15 steel can play a certain role in reducing friction and wear resistance, meanwhile orientation and depth of the texture affect the friction performance. In the fluid simulation, the positively oriented texture can produce higher oil film pressure than the opposite texture. When the texture depth is 20 μm, it has a better friction and wear reduction effect, and the friction coefficient decreases by 65.81% compared with that of the untextured surface, and the amount of wear decreases by 74.19%. This is mainly due to the existence of texture in the friction process to reduce the contact area and the shallower depth of texture can produce a larger oil film pressure in the oil lubrication state, so it has better tribological performance. The research data provide a certain reference for the design and optimization of the cam tappet friction pair.
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How this classification was reachedexpand
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".