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Record W3034422861 · doi:10.18280/rcma.300204

Evaluation of Tensile Strength and Slurry Erosive Behaviour of Jute Reinforced Natural Rubber Based Flexible Composite

2020· article· fr· W3034422861 on OpenAlexvenueno aff
Vishwas Mahesh, Sharnappa Joladarashi, S. M. Kulkarni

Bibliographic record

VenueRevue des composites et des matériaux avancés · 2020
Typearticle
Languagefr
FieldMaterials Science
TopicNatural Fiber Reinforced Composites
Canadian institutionsnot available
FundersSiddaganga Institute of TechnologyNational Institute of Technology Karnataka, Surathkal
KeywordsUltimate tensile strengthComposite materialComposite numberNatural rubberSlurryMaterials science

Abstract

fetched live from OpenAlex

Polymer based natural fiber reinforced composites are finding their applications in almost all fields of engineering. Flexible composites are different class of composite materials finding their usage in secondary structural application such as sacrificial structures like claddings. Mechanical and tribological characterization of the newly developed composites becomes important prior to using them in any engineering application. The present study concentrates on evaluating the tensile and slurry erosive behaviour of flexible composite manufactured by reinforcing naturally available jute fiber in the form of woven fabric in the naturally available sun dried rubber sheets (NR) using compression moulding technique. ASTM D412 standard is used to evaluate the tensile properties of the proposed flexible composite and the NR sheets. The slurry erosive behaviour is assessed using Ducom slurry erosion testing machine. The results reveal that reinforcing jute with natural rubber enhances the tensile strength compared to natural rubber sheet and the wear of the proposed flexible composite is minimal due to inclusion of natural rubber which is elastic in nature.

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)0.001
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 score0.257
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.002
Scholarly communication0.0000.001
Open science0.0010.001
Research integrity0.0000.001
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 head0.078
GPT teacher head0.307
Teacher spread0.230 · 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

Citations6
Published2020
Admission routes1
Has abstractyes

Explore more

Same venueRevue des composites et des matériaux avancésSame topicNatural Fiber Reinforced CompositesFrench-language works237,207