Mean Wear Approach for Modeling and Predicting Wear for Gears in Plastics Materials and their Composites
Bibliographic record
Abstract
It is currently recognized by the scientific and industrial world that gears made of plastic materials and their composites have numerous advantages (light weight and inertia reduction, no lubrication or initial lubrication, low friction coefficient, shock and vibration absorbing, good load distribution, low costing manufacturing, etc. ) and they will continue to beneficially replace metal gears in a good number of applications in all areas; above all, today the family of plastic materials and their composites is expanding with the development of new eco-plastics and their natural fiber composites as an alternative for sustainable development. However, the challenge remains to continue research in the field of these plastic gears and their composites in order to overcome the problems that still hamper their use. The literature reveals that wear constitutes one of the failure modes of gears and in particular it remains the most frequent cause of damage in gears made of plastic materials and their composites. According to the results of experimental work carried out on the wear behavior of plastic gears and their composites, the wear prediction models developed for their metallic counterparts are not applicable to them. The main objective of this present work is to study the wear behavior of gear teeth made of plastic materials and their composites in order to develop a model of its prediction. In this paper, a mean wear approach is used to develop a model based on Archard's law for the prediction of wear in gears made of plastic materials and their composites. The model is built on experimental works observations and depends on the pair of materials and the operating conditions of the mesh, as well as the parameters which are determined once and for all from the initial experimental results. The model also takes into account the very significant thermal effect on the wear of plastic gears. The results from a simulation carried out, using MATLAB software for the pair of HDPE30B materials (HDPE polyethylene composite with 30% birch wood fiber) running under dry conditions, are presented and analyzed. The results are consistent with those of our experimental work and are mainly validated with a relative error below 15% by the latter. The models developed can already provide solutions to needs on an industrial scale.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.001 |
| 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".