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Record W4281617389 · doi:10.3390/coatings12060759

Mechanical Properties and Residual Stress Measurement of TiN/Ti Duplex Coating Using HiPIMS TiN on Cold Spray Ti

2022· article· en· W4281617389 on OpenAlexaff
Nhat Minh Dang, Wen‐Yen Lin, Zhaoying Wang, Sima A. Alidokht, Richard R. Chromik, Terry Yuan-Fang Chen, Ming-Tzer Lin

Bibliographic record

VenueCoatings · 2022
Typearticle
Languageen
FieldEngineering
TopicMetal and Thin Film Mechanics
Canadian institutionsMcGill University
Fundersnot available
KeywordsMaterials scienceTinTitanium nitrideResidual stressCoatingTitaniumMetallurgyHigh-power impulse magnetron sputteringComposite materialTungsten carbideSputter depositionThin filmNitrideSputteringLayer (electronics)Nanotechnology

Abstract

fetched live from OpenAlex

This study investigated the mechanical properties and the residual stress of high-power impulse magnetron sputtering (HiPIMS) titanium nitride (TiN) thin film capping on cold spray titanium (Ti) coating. This TiN/Ti duplex coating was deposited on the Ti substrate, and the cold spray titanium (Ti) coating was prepared in three cases with different numbers of layers. The study determined Young’s modulus, hardness, and roughness of TiN thin film and cold spray Ti coatings by nano-indentation and AFM. The residual stress measurement of TiN/Ti duplex coating was conducted using the ring-core drilling method. A focused ion beam (FIB) drilled the TiN/Ti duplex coating with various milling depth steps. The corresponding images were obtained with a scanning electron microscope (SEM). The relationship between surface deformations and relaxation stress after each milling depth step was obtained using the digital image correlation (DIC) method. The results showed TiN/Ti duplex coating exhibited excellent mechanical properties, and the residual stresses were not significantly changing with different Ti cold spray substrates, showing the feasibility of coating technology for the future applications in the aerospace industry.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.047
Threshold uncertainty score0.734

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.079
GPT teacher head0.216
Teacher spread0.137 · 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.

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

Citations13
Published2022
Admission routes1
Has abstractyes

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