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Record W4376881367 · doi:10.31399/asm.cp.itsc2023p0538

Modeling the Formation of Thermal Spray Coatings on Substrates with Arbitrary Shapes

2023· article· en· W4376881367 on OpenAlexaff
Behrouz Haghighi, Mohammad Passandideh‐Fard, J. Mostaghimi

Bibliographic record

VenueThermal spray · 2023
Typearticle
Languageen
FieldEngineering
TopicHigh-Temperature Coating Behaviors
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsMaterials scienceResidual stressCoatingMicrostructureYttria-stabilized zirconiaSubstrate (aquarium)Composite materialThermal sprayingFinite element methodCubic zirconiaPorosityStress (linguistics)Die (integrated circuit)Surface finishCeramicStructural engineeringNanotechnology

Abstract

fetched live from OpenAlex

Abstract The formation of Nickel coatings on stainless steel substrates and YSZ (Yttria-Stabilized Zirconia) on NiCrAlY in the Atmospheric Plasma Spray (APS) process is investigated. Coating formation over a substrate with an arbitrary shape (an inclined step in this paper) is considered. The topography of the coatings, as well as their microstructure, e.g., porosity, average thickness, and average roughness, are evaluated. An algorithm, which is based on the Monte-Carlo stochastic model, is employed. The significant difference between the coating temperature and that of the substrate leads to the formation of residual thermal stresses. These stresses are analyzed using Object Oriented Finite-element software (OOF) developed by the National Institute of Standards and Technology (NIST). An image of the cross-section of the coating is imported into the code, which utilizes an adaptive meshing technique and Finite- Element Method to calculate residual thermal stresses. The maximum stress in the coatings occurs at the interface between the coating and the substrate. The coatings' topography and microstructure are compared with those of the experiments.

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 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 score0.306
Threshold uncertainty score0.588

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 head0.018
GPT teacher head0.223
Teacher spread0.205 · 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

Citations0
Published2023
Admission routes1
Has abstractyes

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