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Record W4393323081 · doi:10.1051/e3sconf/202450701036

Advancements in Aluminum-Based Composite Manufacturing: Leveraging La2O3 Reinforcement through Friction Stir Process

2024· article· en· W4393323081 on OpenAlexaff
Hawraa Kareem, Hemanth Raju, Errabelli Annapoorna, H. Pal Thethi, Lalit Kumar Tyagi, Vandna Kumari

Bibliographic record

VenueE3S Web of Conferences · 2024
Typearticle
Languageen
FieldEngineering
TopicAluminum Alloys Composites Properties
Canadian institutionsHorizon College and Seminary
Fundersnot available
KeywordsComposite numberFriction stir processingReinforcementAluminiumMaterials scienceProcess (computing)Composite materialManufacturing engineeringComputer scienceEngineeringOperating system

Abstract

fetched live from OpenAlex

This study investigates the advancements in Aluminum-Based Composite Manufacturing through the incorporation of lanthanum oxide (La2O3) reinforcement using the Friction Stir Process (FSP). The pivotal role of precision machining, particularly the vertical milling machine, in executing FSP is emphasized. Specific parameters, including pin diameter, tool tilt angle, and rotational speed, were meticulously selected to ensure optimal performance. The uniform distribution of La2O3 particles within the composite matrix highlights the effectiveness of the fabrication process, indicating proper mixing and dispersion techniques. Experimental findings reveal significant improvements in mechanical properties, with a notable 22.78% enhancement in tensile strength, a significant 35.21% increase in hardness, a noteworthy 24.44% improvement in fatigue strength, and a substantial 28.68% increase in wear resistance observed in aluminum-La2O3 composites produced via FSP. These results underscore the potential of leveraging FSP for aluminum-based composite manufacturing, offering opportunities for the development of high-performance materials with enhanced mechanical properties and durability.

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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.463
Threshold uncertainty score0.932

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 head0.023
GPT teacher head0.251
Teacher spread0.228 · 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

Citations1
Published2024
Admission routes1
Has abstractyes

Explore more

Same venueE3S Web of ConferencesSame topicAluminum Alloys Composites PropertiesFrench-language works237,207