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Record W2972708357 · doi:10.2351/1.5061318

A numerical-experimental investigation on the deposition of Ti-45Nb on mild still using automated laser fabrication process

2008· article· en· W2972708357 on OpenAlexaff
Vahid Fallah, Amir Khajepour, Masoud Alimardani

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicAdditive Manufacturing Materials and Processes
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsMaterials scienceFabricationTitanium alloyDeposition (geology)CoatingCorrosionMicrostructureLaserAlloyBiocompatibilityElastic modulusMetallurgyTitaniumCladding (metalworking)Composite materialOptics

Abstract

fetched live from OpenAlex

Automated Laser Fabrication process (Laser Cladding) has shown tremendous potential for various applications such as rapid prototyping, coating, and production of functionality graded parts. Parallel to other technical challenges involved in this method, exploring different materials and alloys with novel properties that can be adopted for the fabrication process using ALFa is another priority for the research in this field. Ti-45Nb is an attractive alloy because of its low modulus of elasticity, high corrosion resistance, significantly increased threshold oxygen pressure for ignition (from that of pure Titanium) and superior biocompatibility. In this paper, different aspects of the deposition of premixed powder Ti-45Nb on mild still are studied. To gain better insight into the nature of the deposition of this alloy, a 3D time-dependent numerical model is developed in which all material properties are considered to be temperature-dependent. The model is used to correlate the microstructure and the phases formed at different processing parameters with the interaction time between the laser beam and the powder or in turn the heating and the cooling rates during the process.

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 score0.358
Threshold uncertainty score0.350

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 head0.042
GPT teacher head0.255
Teacher spread0.213 · 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
Published2008
Admission routes1
Has abstractyes

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