Failure mechanism of coated biomaterials under high impact-sliding contact stresses
Bibliographic record
Abstract
This study uses a newly developed testing method-- inclined cyclic impact-sliding test to investigate the failure behaviors of different types of biomaterials, (SS316L, Ti6Al4V and CoCr) coated by different coatings (TiN, DLC and PEO), under extremely high dynamic contact stress conditions. This test method can simulate the combined impact and sliding/rolling loading conditions, which is very practical in many aspects of commercial usages. During the tests, fatigue cracking, chipping, peeling and material transferring were observed in damaged area. This research is mainly focused on the failure behaviors of load-bearing materials which cyclic impacting and sliding are always involved. This purpose was accomplished in the three stages: First, impact-sliding test was carried out on TiN coated unhardened M2. It was found that soft substrate can cause early failure of coating due to the considerable plastic deformation in the substrate. In this case, stronger substrate is required to support coating better when tested under high contact stresses. Second, PEO coated Ti-6Al-4V was tested under pure sliding and impact-sliding wear conditions. PEO coating was found not strong enough to afford the high contact pressure under cyclic impact-sliding wear test due to its porous surface structure. However, the wear performance of PEO coating was enhanced due to the sub-stoichiometric oxide. To sum up, for load-bearing biomedical implants involved in high impacting movement, PEO coating may not be a promising surface protection. Third, the dense, smooth PVD/CVD bio-inert coatings were reconsidered. DLC and TiN coatings, combined by different substrates together with different interface materials were tested under the cyclic impact-sliding test using a set of proper loading. The results show that to choose a proper combination of coating, interface and substrate based on their mechanical properties is of great importance under the test condition. Hard substrates provide support to coating better and a ductile and adhesive interface layer can delay the cracked coating from peeled-off.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".