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Record W2947669612 · doi:10.3390/jmmp3020042

Post-Weld Heat Treatment Effects on Mechanical Properties and Microstructure of AA6061-T6 Butt Joints Made by Friction Stir Welding at Right Angle (RAFSW)

2019· article· en· W2947669612 on OpenAlexaff
Mahboubeh Momeni, Michel Guillot

Bibliographic record

VenueJournal of Manufacturing and Materials Processing · 2019
Typearticle
Languageen
FieldEngineering
TopicAdvanced Welding Techniques Analysis
Canadian institutionsUniversité Laval
Fundersnot available
KeywordsFriction stir weldingWeldingMaterials scienceUltimate tensile strengthFusion weldingButt jointTensile testingButt weldingAlloyRepeatabilityMicrostructureMetallographyMetallurgyComposite material

Abstract

fetched live from OpenAlex

Friction stir welding (FSW) provides users with many advantages over fusion welding techniques. Nevertheless, it is not widely employed in current industry mainly due to high equipment costs and royalties. To overcome these issues, a low-cost FSW technique operated at a right angle, called RAFSW, has recently been developed by our research team. To make the RAFSW technique reliable for potential users, we are going to analyze the effect of various post-weld heat treatments (PWHT) on the mechanical and physical properties of the RAFSW joints. To this end, optimized process parameters are used to weld butt joints of an AA6061-T6 alloy. The joints were characterized using a tensile test, a micro-hardness test, and metallography techniques. The most efficient aging time was obtained for various aging temperatures. Moreover, it was found that artificial aging at 220 °C for 30 min could be used as a fast and cost-effective artificial aging PWHT for the industrial sector. In addition, the repeatability of the PWHTs were demonstrated by studying the effect of waiting time prior to the artificial aging. Finally, it was revealed that a single fast artificial aging process is more beneficial than solubilizing followed by an artificial aging process in terms of tensile properties, consumed time, and cost.

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 score0.006
Threshold uncertainty score0.708

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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)0.0000.000

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 head0.006
GPT teacher head0.202
Teacher spread0.196 · 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

Citations14
Published2019
Admission routes1
Has abstractyes

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