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Linear Friction Welding of IN718 to Ti6Al4V

2016· article· en· W2554164140 on OpenAlexaff
Priti Wanjara, Javad Gholipour, Kosuke Watanabe, Koji Nezaki, Yubin Tian, Mathieu Brochu

Bibliographic record

VenueMaterials science forum · 2016
Typearticle
Languageen
FieldEngineering
TopicAdvanced Welding Techniques Analysis
Canadian institutionsMcGill UniversityNational Research Council Canada
FundersIHI
KeywordsMaterials scienceIntermetallicSuperalloyWeldingInconelMetallurgyFriction weldingBrittlenessAlloyBase metalComposite material

Abstract

fetched live from OpenAlex

Linear friction welding (LFW), an emerging automated technology, has potential for solid-state joining of dissimilar materials (bi-metals) to enable tailoring of the mechanical performance, whilst limiting the assembly weight for increased fuel efficiency. However, bi-metallic welds are quite difficult to manufacture, especially when the material combinations can lead to the formation of intermetallic (brittle) phases at the interface, such as the case with assembly of Ti base alloys with Ni base superalloys. The intermetallic phase, once formed, lowers the performance of the as-manufactured properties and its growth during elevated temperature service can lead to unreliable performance. In this project, it was demonstrated that linear friction welding can be applied to join Ti-6%Al-4%V (workhorse Ti alloy) to INCONEL ® 718 (workhorse Ni-base superalloy) with minimized interaction at the interface. Of particular merit is that no intermediate layer (between the Ti alloy and Ni-base superalloy) was needed for bonding. Characterization of the bi-metallic weld included macro-and microstructural examination of the flash and interface regions and evaluation of the hardness.

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 score0.216
Threshold uncertainty score0.246

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.249
Teacher spread0.240 · 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

Citations15
Published2016
Admission routes1
Has abstractyes

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