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Record W2964450951 · doi:10.18280/rcma.290205

Effect of Uncarbonized Eggshell Weight Percentage on Mechanical Properties of Composite Material Developed by Electromagnetic Stir Casting Technique

2019· article· en· W2964450951 on OpenAlexvenueno aff
Shashi Prakash Dwivedi, Nagendra Kumar Maurya, Manish Maurya

Bibliographic record

VenueRevue des composites et des matériaux avancés · 2019
Typearticle
Languageen
FieldEngineering
TopicAdditive Manufacturing and 3D Printing Technologies
Canadian institutionsnot available
Fundersnot available
KeywordsComposite numberComposite materialMaterials scienceEggshellCastingBiology

Abstract

fetched live from OpenAlex

This study deals with the effect of uncarbonized eggshell weight percentage on the tensile strength of AA 2014 composite manufactured by Electromagnetic stir casting technique. Response surface methodology (RSM) is used to design the experiment. The selected process parameters are preheat temperature, stirring current, stirring time, matrix pouring temperature, and reinforcement of uncarbonized eggshell weight percentage. Analysis of variance (ANOVA) is employed to study the influence of selected process parameters. The maximum tensile strength of 287.194 MPa was achieved for reinforcement preheat temperature 537.87 C, stirring current 12 A, stirring time 179.9 sec, matrix pouring temperature 726.8 C, and reinforcement weight percentage of 12.46. At optimum level of process parameter confirmation tests are performed to validate the result. In confirmation test, tensile strength and hardness are investigated. It is observed that due to the reinforcement of uncarbonized eggshell in AA2014 aluminium alloy, tensile strength and hardness are improved by 55.24 % and 39.58 %.

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 score0.026
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 head0.018
GPT teacher head0.229
Teacher spread0.211 · 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

Citations21
Published2019
Admission routes1
Has abstractyes

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