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Record W4409982007 · doi:10.31399/asm.cp.itsc2025p0253

Modeling the Formation of Thermal Spray Coatings on a Rough Substrate

2025· article· en· W4409982007 on OpenAlex
Behrouz Haghighi, Mohammad Passandideh‐Fard, J. Mostaghimi

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

VenueThermal spray · 2025
Typearticle
Languageen
FieldMaterials Science
TopicMaterial Properties and Applications
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsMaterials scienceSubstrate (aquarium)Thermal sprayingThermalChemical engineeringComposite materialCoatingEngineeringThermodynamicsGeologyPhysics

Abstract

fetched live from OpenAlex

Abstract Thermal spray coatings are typically applied to grit-blasted, rough surfaces, though coating models generally assume smooth substrates. This research involved simulating nickel coating formation on rough stainless-steel substrates in an atmospheric plasma spray process. The researchers evaluated coating topography, porosity, thickness, and roughness using a Monte-Carlo stochastic algorithm. The temperature differential between coating and substrate creates residual thermal stresses, which were analyzed using NIST's Object Oriented Finite element software (OOF). Results indicate that substrate roughness increases coating roughness and creates non-uniform stress distribution with concentration points at the coating-substrate interface.

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)0.000
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 score0.016
Threshold uncertainty score0.394

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 head0.026
GPT teacher head0.254
Teacher spread0.228 · 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