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Record W2897116296 · doi:10.2351/1.5062043

Correlation between temperature distribution and formed microstructure of in-situ laser cladding of Fe-TiC on carbon steel

2010· article· en· W2897116296 on OpenAlexaff
Ali Emamian, Masoud Alimardani, Stephen F. Corbin, Amir Khajepour

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicHigh Entropy Alloys Studies
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsMaterials scienceMicrostructureHardfacingCladding (metalworking)Carbon steelCoatingComposite numberComposite materialGraphiteMetallurgyLayer (electronics)TitaniumCorrosion

Abstract

fetched live from OpenAlex

One of the unique aspects of in-situ laser cladding is to create a uniform clad by melting the powder and a thin layer of the substrate to form a composite of pure powder components with minimal dilution. Therefore, this technique can be an excellent candidate for hardfacing process by deposition of multiple clad beads side by side on a low cost base material. Since TiC has desirable properties such as hardness, wear and corrosion resistance, in this work, the hardfacing process of AISI 1030 carbon steel using titanium (Ti) and graphite (C) as a composite coating material (i.e., Fe(Ti)-TiC) is investigated using a numerical and experimental analysis. In order to study the microstructure of the TiC morphology and distribution in the clad, a 3D time-dependent numerical model and ternary phase diagram are used to interpret the experimental results along with the temperature distributions formed throughout the deposition process. The morphology and distribution of TiC particles are studied by means of SEM, XRD.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.639
Threshold uncertainty score0.364

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 head0.004
GPT teacher head0.200
Teacher spread0.196 · 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

Citations2
Published2010
Admission routes1
Has abstractyes

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