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Record W2469224221

Overview of Laser and Linear Friction Welding Processes for Ti-5553

2013· article· en· W2469224221 on OpenAlexvenueaboutno aff
X. Cao

Bibliographic record

VenueNPARC · 2013
Typearticle
Languageen
FieldEngineering
TopicWelding Techniques and Residual Stresses
Canadian institutionsnot available
Fundersnot available
KeywordsWeldingMaterials scienceLaserMetallurgyPhysicsOptics
DOInot available

Abstract

fetched live from OpenAlex

Light weighting of primary aircraft structures has resulted in ever increasing transition from aluminum alloys to composite materials. Nonetheless, the regions of stress concentration in the composite materials need local reinforcement with metallic structures. Of the various possibilities, titanium alloys offer the highest electrochemical compatibility, with a concomitant high strength to weight ratio, but their high raw material cost and relatively poor machinability and formability are strong motivators to introduce emerging manufacturing technologies that allow a reduction in the buy to fly ratio (i.e. minimized scrap). Hence, the development of cost efficient joining technologies has become an indispensable challenge for the design and near net shape processing of titanium alloy structures. Arc welding, including plasma, has been the traditional joining process used for titanium alloys. However, the high reactivity of titanium with atmospheric gases at elevated temperatures above 400C, especially in the liquid state, has led to the use of high vacuum electron beam welding, particularly for the aerospace industry. With the development of high power solid-state lasers and solid-state linear friction welding, these advanced joining processes have shown significant potential for titanium alloys. In recent years, the Aerospace branch of the National Research Council of Canada (NRC) has conducted some fundamental studies to understand the weldability of a new aerospace titanium alloy, Ti5Al5V5Mo3Cr, using high power solid-state laser and solid-state linear friction welding processes. This presentation will summarize the important progresses achieved in this field. (The authors acknowledge T. Shariff (master) and E. Dalgaard (Ph.D.) and their supervisors Profs. R. Chromik and J.J. Jonas from McGill University; NRC staff E. Poirier, M. Guerin, D. Chiriac, X. Pelletier, J. Baradari and M. Jahazi; Standard Aero Limited staff J. Cuddy and A. Birur.)

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)0.001
metaresearch head score (Gemma)0.000
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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.010
Threshold uncertainty score0.032

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0030.002
Science and technology studies0.0010.000
Scholarly communication0.0010.002
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0100.007

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 head0.258
Teacher spread0.232 · 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

Citations0
Published2013
Admission routes2
Has abstractyes

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