MétaCan
Menu
Back to cohort
Record W3038094296 · doi:10.1177/0954406220931527

Analysis of tensile-shear strength of single and multi-friction stir spot welding joints under fixed welding process conditions

2020· article· en· W3038094296 on OpenAlexaff
Morteza Asadollahi, Neda Jabbari, Soheil Nakhodchi, Hossein Salimi, Hamed Haddad Khodaparast

Bibliographic record

VenueProceedings of the Institution of Mechanical Engineers Part C Journal of Mechanical Engineering Science · 2020
Typearticle
Languageen
FieldEngineering
TopicAdvanced Welding Techniques Analysis
Canadian institutionsYork University
Fundersnot available
KeywordsSpot weldingWeldingMaterials scienceUltimate tensile strengthFriction stir weldingComposite materialShear (geology)Friction weldingMetallurgy

Abstract

fetched live from OpenAlex

The tensile-shear strength of AA 5052 single and multi-friction stir spot welding joints were analyzed using experimental, numerical, and analytical approaches. Benchmark specimens were designed and manufactured in a similar manner with respect to industrial practice. Under the fixed welding process condition, the failure mechanism of friction stir spot welded specimens under tensile-shear loading was first determined by using macro- and micro-structural analysis. It is shown that increasing the tool shoulder diameter and the number of friction stir spot weldings may nonproportionally increase the strength of the joints. In the linearly arranged multi-friction stir spot welding joints, the strength of these joints was discussed using analytical approach. It is demonstrated that in certain cases, increasing the nugget diameter is preferred than increasing the number of nuggets. This is only applicable to a certain friction stir spot welding failure mechanism. A finite element model prediction tool was developed to predict the tensile-shear strength of friction stir spot welded joints using the material properties obtained from the measurement of experimental hardness.

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)0.001
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
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 score0.318
Threshold uncertainty score0.956

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.003
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
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 head0.023
GPT teacher head0.255
Teacher spread0.232 · 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 designSimulation or modeling
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

Citations5
Published2020
Admission routes1
Has abstractyes

Explore more

Same venueProceedings of the Institution of Mechanical Engineers Part C Journal of Mechanical Engineering ScienceSame topicAdvanced Welding Techniques AnalysisFrench-language works237,207