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Record W3040639917 · doi:10.32620/aktt.2020.3.04

SURFACE REPAIR OF AIRCRAFT TITANIUM ALLOY PARTS BY COLD SPRAYING TECHNOLOGY

2020· article· en· W3040639917 on OpenAlexfundno aff
Wenjie Hu, Sergii Markovych, Kun Tan, Oleksandr Shorinov, Tingting Cao

Bibliographic record

VenueAerospace technic and technology · 2020
Typearticle
Languageen
FieldEngineering
TopicHigh-Temperature Coating Behaviors
Canadian institutionsnot available
FundersBaosteel Group CorporationDalian University of TechnologyUniversity of South ChinaChongqing UniversityUniversity of Ottawa
KeywordsMaterials scienceThermal sprayingMetallurgyGas dynamic cold sprayTitanium alloyCoatingTitaniumCorrosionSupersonic speedAlloyComposite materialAerospace engineeringEngineering

Abstract

fetched live from OpenAlex

Titanium alloys have the advantages of high specific strength, good corrosion resistance, high heat resistance, and low density, which is the main structural material of aerospace system components, including compressor blade, cartridge receiver, blisk, engine nacelle, thermal baffle and so on. At present, about three-quarters of titanium and titanium alloys in the world are used in the aerospace industry, including A350 for 14%, F18 for 15 %, B787 for 15 %, SU-57 for 18 %, J-20 for 20 %, FC-31 fighters for 25 %, F35 for about 27 %, and F22 up to 41 %, etc, so it has the reputation of "space metal". However, its low wear resistance limits the further development of titanium alloy. Besides, its high manufacturing cost, if only require the occasion of surface performance can reduce the use of the substrate, and then reduced the cost. Therefore, the study of aircraft titanium alloy is of great significance, the protection of titanium alloy includes alloying technology and coating technology. Alloying technology mainly adds other elements on its basis to improve the performance, while the most popular method is coating technology, the present, there are many coating technologies, include high-velocity oxy-fuel (HVOF), HVAF, cold spraying, laser cladding, laser micro-fusion in-situ synthesized technology, micro-arc oxidation, laser melt injection (LMI), supersonic laser deposition (SLD) and supersonic plasma spray technology, surface repair titanium alloy parts by cold spraying technology are good ways to solve those problems. Because of its low process temperature, no oxidation, only plastic deformation, and repair efficiency are high, the protective coating has high bonding strength and good impact toughness. In this paper, the types and applications of aircraft titanium alloys were reviewed, the latest research results of surface repair of titanium alloys parts by cold spraying technology were reviewed, technological parameters of the cold gas dynamic spraying technology was analyzed, including powder size of particles, morphologies, critical velocity, particle compression rate, substrate preheating effects on the particle/substrate adhesion, etc.

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 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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
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 score0.028
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.002
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
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 head0.007
GPT teacher head0.208
Teacher spread0.201 · 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 teacher head, not a consensus.

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

Citations13
Published2020
Admission routes1
Has abstractyes

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