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Record W4404733984 · doi:10.1016/j.jalmes.2024.100134

Thermo-microstructural-mechanical modeling of the effect of wire diameters on single-bead Ti-6Al-4V wall deposits by laser wire deposition

2024· article· en· W4404733984 on OpenAlexafffund
Qi Zhang, Nejib Chekir, Mathieu Brochu

Bibliographic record

VenueJournal of Alloys and Metallurgical Systems · 2024
Typearticle
Languageen
FieldEngineering
TopicAdditive Manufacturing Materials and Processes
Canadian institutionsMcGill University
FundersNatural Sciences and Engineering Research Council of CanadaConsortium de Recherche et d’innovation en Aérospatiale au Québec
KeywordsBeadMaterials scienceDeposition (geology)Composite materialLaserMetallurgyOpticsGeology

Abstract

fetched live from OpenAlex

Six Ti-6Al-4V deposits with two geometries to extract tensile coupons from the build direction (Z) and travel direction (X), were produced by laser wire deposition (LWD) with different wire diameters. Inconsistent wall profiles were found for deposits produced with wire diameters of 1.1 and 1.6 mm due to the inhomogeneous melt pool sizes generated during the deposition process, indicating the unstable thermal history. It was found that increasing the wire diameter resulted in increased heat input, which produced coarser grains and resulted in decreased strength. Fractography analysis showed that greater amounts of defects were observed on the fracture surfaces of deposits produced with increased wire diameters, which weakened the tensile properties. The grain boundary α and the surrounding colony α facilitated crack propagation and caused early failure. This research also proposed a platform using modeling techniques to control cooling rates, microstructure ( α / β phase fractions and α lath widths) and yield strength of LWD Ti-6Al-4V. The accuracy of all models was validated by comparing them with experimental data.

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 score0.008
Threshold uncertainty score0.441

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
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 head0.008
GPT teacher head0.198
Teacher spread0.190 · 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
Published2024
Admission routes2
Has abstractyes

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