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Record W4399963962 · doi:10.3390/coatings14070786

A Review of Research on Improving Wear Resistance of Titanium Alloys

2024· review· en· W4399963962 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueCoatings · 2024
Typereview
Languageen
FieldMaterials Science
TopicTitanium Alloys Microstructure and Properties
Canadian institutionsUniversity of Regina
FundersNational Natural Science Foundation of China
KeywordsMaterials scienceWear resistanceMetallurgyTitaniumResistance (ecology)Titanium alloyAlloy

Abstract

fetched live from OpenAlex

Titanium alloy is widely used as oil drill pipe material because of its light weight, high strength, good toughness, corrosion resistance, fatigue resistance, and good process performance. However, due to its low hardness, poor wear resistance, serious oxidation at high temperature (700 °C), and difficulty in lubrication, in oil and gas field exploration and development drilling, especially in deep wells, high displacement wells, horizontal wells, and highly deviated wells, wear and tear are prone to occur. The application and development of titanium alloys are greatly limited. This paper introduces the research status of the common surface modification technologies of titanium alloys, such as laser cladding, magnetron sputtering, plasma spraying, micro arc oxidation, etc. It points out the improvement effect of various modification technologies on the wear resistance and high-temperature oxidation resistance of titanium alloys and discusses the advantages and disadvantages of various modification technologies. A proposed method for enhancing the wear resistance and high-temperature oxidation resistance of titanium alloys was finally introduced, and its potential for future development was investigated.

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.

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.607
Threshold uncertainty score0.924

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.102
GPT teacher head0.398
Teacher spread0.296 · 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