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Record W3117954038 · doi:10.18280/acsm.440608

Effect of Multiple Passes on Microstructural and Mechanical Properties of Surface Composite Al 2024/SiC Produced by Friction Stir Processing

2020· article· en· W3117954038 on OpenAlexvenueno aff
Ashish Kumar Srivastava, Nagendra Kumar Maurya, Manish Maurya, Shashi Prakash Dwivedi, Ambuj Saxena

Bibliographic record

VenueAnnales de Chimie Science des Matériaux · 2020
Typearticle
Languageen
FieldEngineering
TopicAluminum Alloys Composites Properties
Canadian institutionsnot available
Fundersnot available
KeywordsNanoindentationMaterials scienceRockwell scaleFriction stir processingComposite numberComposite materialUltimate tensile strengthMetal matrix compositeTensile testingOptical microscopeIndentation hardnessAluminiumMicrostructureScanning electron microscope

Abstract

fetched live from OpenAlex

The application range in defense, aerospace and automotive sectors have enabled aluminium metal matrix composites to emerge in different technological fields due to enhanced micro structural and mechanical characteristics. In the present study, friction stir processing is used to fabricate Al2024/SiC composite with one, two and three passes of the cylindrical tool. Optical microscopy and scanning electronic microscope (SEM) were used to validate the processed sample and to justify the morphological aspects. Energy dispersive spectroscopy (EDS) analysis has also performed to confirm the presence of SiC particles in the composite. It also includes the analysis of mechanical properties such as tensile strength, Rockwell hardness test and nanoindentation to characterize the prepared samples. Improvement in tensile strength with a maximum of 443 MPa, the hardness of 121 HRB and nanoindentation of the specimen was depicted through the mechanical tests.

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 score0.694

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 studies0.0000.001
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 head0.022
GPT teacher head0.232
Teacher spread0.210 · 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

Citations22
Published2020
Admission routes1
Has abstractyes

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