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Record W4280608553 · doi:10.1016/j.matpr.2022.05.057

Effect of ultrasonic vibration treatment on microstructure and dry sliding wear characteristics of LM25 aluminum alloy

2022· article· en· W4280608553 on OpenAlexfundno aff
Rajkumar Wagmare, T.V. Anilkumar, N. D. Prasanna

Bibliographic record

VenueMaterials Today Proceedings · 2022
Typearticle
Languageen
FieldEngineering
TopicAluminum Alloy Microstructure Properties
Canadian institutionsnot available
FundersDepartment of Mechanical Engineering, University of AlbertaRamaiah Institute Of Technology
KeywordsMaterials scienceEquiaxed crystalsAlloyMicrostructureAluminiumMetallurgyGrain sizeUltrasonic sensorVibrationComposite materialAcoustics

Abstract

fetched live from OpenAlex

Grain refinement studies on Aluminum alloys with the addition of silicon, and magnesium alloys have fascinated considerable attention in the last five decades. Aluminum alloy properties of castings predominately depend on the grain structure formation that takes place inside the material meanwhile the solidification process. In the current investigation, a device has been designed and established to induce high-frequency ultrasonic vibrations to the molten metal at the time of solidification. This paper outlines Ultrasonic frequencies of varied were 10 to 50 kHz in the step of 10 kHz. The amplitude of the experiment was kept constant at 15 V for the entire findings. From the studies, it is found that the size of the grains has been reduced by 40% from coarse to fine equiaxed grains. Hardness value was noted with increased by 57.5% as well as Wear properties of treated LM25 alloy in dry sliding conditions have substantially improved. Wear studies has been carried out using standard pin on disc machine with various altered speeds. The study aims to validate that there are grain refinement and considerable improvement in the wear-resistant properties of the treated alloy.

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 categoriesMeta-epidemiology (narrow)
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 score0.002
Threshold uncertainty score1.000

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 head0.005
GPT teacher head0.191
Teacher spread0.186 · 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.

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

Citations1
Published2022
Admission routes1
Has abstractyes

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