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Record W3189990318 · doi:10.3390/ma14164499

Effect of Direct Powder Forging Process on the Mechanical Properties and Microstructural of Ti-6Al-4V ELI

2021· article· en· W3189990318 on OpenAlexaff
Sébastien Germain Careau, Bernard Tougas, Elena Ulate-Kolitsky

Bibliographic record

VenueMaterials · 2021
Typearticle
Languageen
FieldMaterials Science
TopicTitanium Alloys Microstructure and Properties
Canadian institutionsUniversité du Québec à Trois-Rivières
Fundersnot available
KeywordsForgingMaterials sciencePowder metallurgyMetallurgyTitanium powderTitaniumConsolidation (business)Titanium alloyMicrostructureAlloyAnnealing (glass)Composite material

Abstract

fetched live from OpenAlex

The study of powder metallurgy processing methods for titanium represents a promising avenue that can respond to a growing demand. This work reports the feasibility of direct powder forging (DPF) as a method to process large spherical Ti-6Al-4V powder into wrought products with noteworthy properties and physical characteristics. Direct powder forging is a thermomechanical process that imparts uniaxial loading to an enclosed and uncompacted powder to produce parts of various sizes and shapes. Stainless steel canisters were filled with prealloyed Ti-6Al-4V powder and consolidated through a multi-step open-die forging and rolling process into wrought DPF bars. After DPF, annealing was performed in the upper α+β phase. The results show that full consolidation was achieved and higher mechanical properties than the Ti-6Al-4V grade F-23 requirements in annealed conditions were obtained. The results also show that direct powder forging of spherical titanium powder could produce wrought mill products and exhibit some potential for further investigation for industrial applications.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.001
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
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)0.0010.000

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 head0.014
GPT teacher head0.234
Teacher spread0.220 · 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 source (direct Gemma or distilled Codex), 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

Citations10
Published2021
Admission routes1
Has abstractyes

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