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Record W3139104009 · doi:10.18280/rcma.310108

Analysis of Impact Behaviour of Sisal-Epoxy Composites under Low Velocity Regime

2021· article· en· W3139104009 on OpenAlexvenueno aff
Vishwas Mahesh, Ashutosh Nilabh, Sharnappa Joladarashi, S. M. Kulkarni

Bibliographic record

VenueRevue des composites et des matériaux avancés · 2021
Typearticle
Languageen
FieldMaterials Science
TopicNatural Fiber Reinforced Composites
Canadian institutionsnot available
FundersScience and Engineering Research BoardNational Institute of Technology Karnataka, SurathkalDepartment of Science and Technology, Ministry of Science and Technology, India
KeywordsEpoxySISALMaterials scienceComposite materialComposite numberImpact energyTaguchi methods

Abstract

fetched live from OpenAlex

The present study concentrates on development of conceptual proof for sisal reinforced polymer matrix composite for structural applications subjected to low velocity impact using a finite element (FE) approach. The proposed sisal-epoxy composite of various thicknesses of 3.2 mm, 4 mm and 4.8 mm is subjected to different impact velocities of 1 m/s, 2 m/s and 3 m/s ranging in the low velocity impact regime to study the energy absorbed and damage mitigation behaviour of the proposed composite. The consequence of velocity of impact and thickness of laminate on the sisal epoxy composite’s impact behaviour is assessed statistically using Taguchi’s experimental design. Outcome of the present study discloses that the energy absorption increases with increased impact velocity and laminate thickness. However, the statistical study shows that impact velocity is predominant factor affecting the impact response of sisal epoxy composite laminate compared to laminate thickness. The role of matrix and fiber in damage initiation is studied using Hashin criteria and it is found that matrix failure is predominant over the fiber failure.

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)0.000
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 score0.103
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0010.002
Science and technology studies0.0000.001
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.040
GPT teacher head0.308
Teacher spread0.268 · 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

Citations12
Published2021
Admission routes1
Has abstractyes

Explore more

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