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Record W3014438948 · doi:10.82308/46705

Role of synergy between wear and corrosion in degradation of materials

2008· article· en· W3014438948 on OpenAlexaboutno aff
M. Azzi

Bibliographic record

VenueeScholarship@McGill (McGill) · 2008
Typearticle
Languageen
FieldEngineering
TopicTribology and Wear Analysis
Canadian institutionsnot available
Fundersnot available
KeywordsDegradation (telecommunications)MetallurgyCorrosionForensic engineeringMaterials scienceBusinessEnvironmental scienceEngineering

Abstract

fetched live from OpenAlex

Tribocorrosion is a term used to describe the material degradation due to the combination of electrochemical and tribological processes. Due to a synergetic effect, the material loss can be larger than the sum of the losses due to wear and corrosion acting separately. In this thesis, the synergy of wear and corrosion was investigated for different types of material, namely the Ti-6Al-4V alloy, the SS316L stainless steel coated with a thin film of Diamond Like Carbon (DLC), and the SS301 stainless steel coated with a thin film of chromium silicon nitride (CrSiN). A tribocorrosion apparatus was designed and constructed to conduct wear experiments in corrosive media. Sliding ball-on-plate configuration was used in this design, where the contact between the ball and the specimen is totally immersed in the test electrolyte. The specimen was connected to a potentiostat to control its electrochemical parameters, namely the potential and the current. Electrochemical techniques were used to control the kinetics of corrosion reactions, and therefore it was possible to assess separately the role of corrosion and wear in the total degradation of material, and to evaluate the synergy between them. For Ti-6Al-4V, it was found that the corrosion and tribocorrosion depend strongly on the structure of the material. The alpha-equiaxed microstructure with fine dispersed beta-phase exhibited the best corrosion resistance. The corrosion resistance was found to decrease when the basal plane was preferentially aligned parallel to the surface, which is attributed to a low resistance to charge transfer in the oxide films formed on this plane. On the other hand, when wear and corrosion were involved simultaneously, the oxide layer protecting the substrate against dissolution was mechanically destroyed leading to a high corrosion rate. It was found that the hardness was the most important factor determining the tribocorrosion behavior of the Ti-6Al-4V alloy; samples with high hardness exhibited less mechanical wear, less wear-enhanced corrosion, and less corrosion-enhanced wear. For DLC coatings, it was found that interface engineering plays a crucial role in the tribocorrosion behavior of DLC films. DLC films with nitrided interface layer (SS\N3h\DLC) were shown to have very poor tribocorrosion resistance; the DLC film delaminated from the substrate after 50 cycles of sliding wear at 9 N load in Ringer's solution. It should be mentioned that a previous study performed at Ecole Polytechnique de Montreal [4] has shown that the same coating resisted 1800 cycles of dry wear at 22 N without delamination. This demonstrates clearly the effect of corrosion on the wear resistance of DLC films. The use of a-SiN:H bond layer between the SS316L substrate and the DLC film improved significantly the tribocorrosion behavior of the coating. This layer acts as a barrier against corrosion reaction; the polarization resistance was 5.76 GO.cm2 compared to 27.5 MO.cm2 and 1.81 MO.cm2 for the DLC-coated SS316L with nitrided interface layer and the bare substrate, respectively. For CrSiN coatings, it was also shown that nitriding treatment of the substrate prior to deposition reduces significantly the tribocorosion resistance of the CrSiN-coated SS301 substrates. This is attributed to the peculiar morphology of the nitrided surface prior to deposition. The high relives at the grain boundaries of the substrate may be the reason for the generation, during sliding wear, of defects in the film, which makes the infiltration of the liquid easier, and consequently leads to the destruction of the CrSiN film.

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.011
GPT teacher head0.196
Teacher spread0.185 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations2
Published2008
Admission routes1
Has abstractyes

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