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Record W2743692183 · doi:10.21013/jte.icsesd201736

Review of Experimental Investigations in Friction Welding Technique

2017· article· en· W2743692183 on OpenAlexaff
Gurunath Shinde, P. S. Dabeer

Bibliographic record

VenueIRA-International Journal of Technology & Engineering (ISSN 2455-4480) · 2017
Typearticle
Languageen
FieldEngineering
TopicAdvanced Welding Techniques Analysis
Canadian institutionsTrinity College
Fundersnot available
KeywordsWeldingFriction weldingUpsetFlywheelInertiaMechanical engineeringRotational speedMaterials scienceForgingWork (physics)Rotation (mathematics)MetallurgyEngineeringComputer sciencePhysics

Abstract

fetched live from OpenAlex

<p>Friction welding is a solid state welding processes in which the weld is obtained by the heat generated due to forging and friction. Now a day’s eco-friendly joining of dissimilar materials is the need of the industries. The advantages of friction welding process are reduction in production time and cost saving. Friction welding is classified into two types. One type is Inertia drive friction welding and the other is Continuous drive friction welding. In continuous drive friction welding one of the work pieces is held stationary while the other is held for a certain rotating speed. The two work pieces are brought together under certain friction pressure for a<br />certain period of time known as friction time. Then, the rotation is stopped and upset pressure is applied for a certain upset time. Then, the spindle is disengaged and the component is unloaded. In Inertia drive friction welding one part is held stationary while the other is clamped in the chuck which is attached to the flywheel. The flywheel and chuck is rotated for a certain seed to store a predetermined energy. In this paper, review of friction welding on different materials and their weld ability has been discussed in brief.</p>

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.596
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.001
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 head0.011
GPT teacher head0.287
Teacher spread0.276 · 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.

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
Published2017
Admission routes1
Has abstractyes

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