Tribological Investigations of PVD Coated Multi-Layer Constructions
Bibliographic record
Abstract
<p class="1Body">Multi-layer constructions become more and more relevant in lightweight applications due to their high strength to weight ratio. They offer excellent crash, damping and recycling properties. Also, the morphology of thermoplastic carbon fibre reinforced plastics (CFRP) render them interesting for large scale manufacturing processes. Nevertheless, a major disadvantage results in a poor resistance against wear and tear, e.g. erosion, which is attributed to weak hardness properties. Hence, this work deals with tribological investigations on orthotropic carbon fibre reinforced polymers (PA 6) either with protective ceramic coating or without. The chosen coating system is a well-known protective covering of metal components, e.g. metal cutting tools, produced by physical vapor deposition (PVD). To characterize the coating system on thermoplastic CFRP, standard analyzing methods are utilized, like optical and scanning electron microscopy (SEM). The tribological investigations are conducted by the tribological ball on disk method to generate wear tracks on the sample surfaces and hence to calculate the wear rates. These results are compared to literature findings with respect to a certain protective coating system (TiN) and a second nano-structured gel coating system, where both systems are deposited on a thermosetting material, i.e. carbon fibre reinforced epoxy resin, respectively. For this purpose the feasibility of depositing a protective ceramic layer on thermoplastic CFRP is demonstrated. First results on suitable surface pre-treatments have shown a significant influence on the coating quality. The improved performance regarding the wear behavior with respect to tribology compared to the poor substrate and existing technologies is shown additionally.</p>
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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 | 0.025 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.005 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 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 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".