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Record W2944928951 · doi:10.1139/tcsme-2019-0027

Investigation of the microstructural, mechanical, and thermal evolution of dissimilar aluminium alloys during friction stir welding

2019· article· en· W2944928951 on OpenAlexvenueno aff
T. Kasirajan, R. Ravindran, T. Ramkumar, M. Selvakumar

Bibliographic record

VenueTransactions of the Canadian Society for Mechanical Engineering · 2019
Typearticle
Languageen
FieldEngineering
TopicAdvanced Welding Techniques Analysis
Canadian institutionsnot available
Fundersnot available
KeywordsFriction stir weldingMaterials scienceWeldingRotational speedMicrostructureUltimate tensile strengthMetallurgyHeat-affected zoneComposite materialAluminiumBendingJoint (building)Tensile testingMechanical engineeringStructural engineering

Abstract

fetched live from OpenAlex

This work deals with the joining of 6 mm thick dissimilar aluminium plates (AA5083-H111 and AA6082-T6) using a friction stir welding method and by varying the process parameters. Test experiments were performed to identify the influence of process parameters on the joint efficiency of the weldments. The process parameters such as tool rotation speed and tool pin profile were varied; whereas, tool travel speed, tilt angle, and axial force were kept constant for all weldments. Microstructure evaluation was carried out using light optical and scanning electron microscopes, which exposed the grain refinement in the nugget zone (NZ) and thermo-mechanical affected zone (TMAZ). Mechanical property tests for tensile strength, hardness, and bending were performed to understand the influence of the parameters over the weldments. Heat development between the tool shoulder and workpiece was analyzed by calculating the heat flow and heat flux. The thermal diffusivity of AA5083-H111 and AA6082-T6 were calculated to understand the influence of heat distribution in the joint efficiency of the weldments. It is inferred from the current study that the threaded cylinder tool at a rotational speed of 900 rpm achieved the highest tensile strength, hardness, and bend strength over the combination of other parameters.

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 score0.149
Threshold uncertainty score0.487

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.183
Teacher spread0.177 · 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

Citations7
Published2019
Admission routes1
Has abstractyes

Explore more

Same venueTransactions of the Canadian Society for Mechanical EngineeringSame topicAdvanced Welding Techniques AnalysisFrench-language works237,207