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Record W3084925950 · doi:10.14447/jnmes.v23i2.a07

Eco-Friendly Frictional Joining of AA6063 and AISI304L Dissimilar Metals and Characterisation of Bimetal Joints

2020· article· en· W3084925950 on OpenAlexvenueno aff
Haq A. Noorul, P. Sathiya

Bibliographic record

VenueJournal of New Materials for Electrochemical Systems · 2020
Typearticle
Languageen
FieldEngineering
TopicAdvanced Welding Techniques Analysis
Canadian institutionsnot available
Fundersnot available
KeywordsBimetalMaterials scienceMetallurgyComposite material

Abstract

fetched live from OpenAlex

This study discusses the dissimilar joining between AA6063 aluminium and AISI304L stainless steel of flat faying surfaces through a rotary friction welding process at different welding conditions as per L9 orthogonal array and the characterization of bimetal joints. The effect of welding parameters on the mechanical properties and the minimum level of welding conditions required for the metals joining were discussed. Bonding between the metals and the narrow heat-affected zone were observed from microscopy study. Energy dispersive x-ray analysis revealed the elements present at the weld interface and molybdenum formation. Fractography result on the tested specimens showed the dimple rupture during the fracture. The tensile test showed the plastic deformation near the weld joint for friction pressure 15 MPa and above. The maximum peak load and elongation of joint were around 12 kN and 12% respectively. 18 MPa friction pressure, 24 MPa upset pressure and 5 sec. friction time showed good bond strength and the maximum tensile strength of 189 MPa with 92 % of joint efficiency. Axial shortening was obtained in the range of 13-27 mm and a maximum of 26.9 mm during experiments. In joints, microhardness was decreasing towards AISI304L base metal from weld interface and conversely increasing towards AA6063 base metal. Impact tests showed the conversion of brittle into ductile fractures when increasing friction pressure and maximum impact energy observed was 38 Joule.

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 score0.077
Threshold uncertainty score0.491

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 head0.014
GPT teacher head0.236
Teacher spread0.222 · 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

Citations9
Published2020
Admission routes1
Has abstractyes

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