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Record W4362712157 · doi:10.5267/j.esm.2023.2.003

Rotary Friction Welding Parameters Effects upon Mechanical Properties and Microstructure of AA2024 Weld Joints

2023· article· en· W4362712157 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

venuePublished in a venue whose home country is Canada.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueEngineering Solid Mechanics · 2023
Typearticle
Languageen
FieldEngineering
TopicAdvanced Welding Techniques Analysis
Canadian institutionsnot available
Fundersnot available
KeywordsWeldingMaterials scienceFriction weldingUltimate tensile strengthJoint (building)Fracture (geology)Composite materialMicrostructureRotational speedForgingFriction stir weldingMetallurgyStructural engineeringMechanical engineeringEngineering

Abstract

fetched live from OpenAlex

The present work investigates Rotary Friction Welding (RFW) of AA2024 similar joints, where the welding operations were carried out using an adapted device to a column-drilling machine (SUPEMEC J320). This work aims to determine the optimal RFW parameters from an experimental study based on mechanical tests and microscopic observations of weld joints. The best compromise, which offers the highest tensile strength value (499 MPa) corresponds to the joint obtained by using a rotational speed of 2000 rpm, a friction pressure of 12 MPa, and a forging pressure of 17 MPa. The EDX results indicate the presence of the intermetallic compounds (IMCs) in each zone of the weld joints, with varying sizes and a random distribution. This study focuses also on the microscopic analyses of the interface of weld joints and the fracture surfaces that indicates a dominant ductile fracture mode.

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.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.290
Threshold uncertainty score1.000

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 head0.010
GPT teacher head0.207
Teacher spread0.197 · 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