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http://hdl.handle.net/11129/6460
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Title: | Experimental and Numerical Investigation on The Elastic Properties of Natural Fiber Composites |
Authors: | Zeeshan, Qasim (Co-Supervisor) Safaei, Babak (Supervisor) Alhijazi, Mohamad Eastern Mediterranean University, Faculty of Engineering, Dept. of Mechanical Engineering |
Keywords: | Thesis Tez Mechanical Engineering Material--Materials Engineering Viscoelastic materials Natural Fibers Composites Palm Fibers Luffa Fibers Thermoplastics and Thermosets Matrices Numerical and Analytical Simulation Machine Learning |
Issue Date: | Aug-2021 |
Publisher: | Eastern Mediterranean University (EMU) - Doğu Akdeniz Üniversitesi (DAÜ) |
Citation: | Alhijazi, Mohamad. (2021). Experimental and Numerical Investigation on The Elastic Properties of Natural Fiber Composites. Thesis (Ph.D.), Eastern Mediterranean University, Institute of Graduate Studies and Research, Dept. of Mechanical Engineering, Famagusta: North Cyprus |
Abstract: | In recent years, the application of natural fibers as reinforcement in composite
structures has received increasing attention due to their advantages of low cost,
environmental friendliness and favorable biocompatibility over synthetic fiber
composite materials. The present work is an investigation on the tensile properties of
palm as well as luffa natural fiber composites (NFC) in high density polyethylene
(HDPE), polypropylene (PP), epoxy, and ecopoxy (BioPoxy 36) matrices, taking into
consideration the effect of fibers volume fraction (Vf) variation. Finite element
analysis i.e. representative volume element (RVE) models with unidirectional and
chopped random fiber orientations, as well as analytical simulation i.e. Rule of Mixture
(ROM), Halpin-Tsai, Chamis, and Nielsen approaches were utilized for predicting the
elastic properties. Tensile test following ASTM D3039 standard was conducted.
Artificial Neural Network (ANN), Multiple Linear Regression (MLR), Adaptive
Neuro-Fuzzy Inference System (ANFIS), and Support Vector Machine were
implemented for defining the design space upon the considered parameters and
evaluating the reliability of these machine learning approaches in predicting the tensile
strength of natural fibers composites. Furthermore, biopoxy 36 with 0.3 luffa fibers
exhibited the highest tensile strength. Finite element analysisfindings profusely agreed
with the experimental results. ANFIS machine learning (ML) tool showed least
prediction error in predicting tensile strength of natural fibers composites. ÖZ:
Son yıllarda, doğal liflerin kompozit yapılarda takviye olarak uygulanması, sentetik
lifli kompozit malzemelere göre düşük maliyet, çevre dostu olma ve uygun
biyouyumluluk avantajları nedeniyle artan bir ilgi görmüştür. Bu çalışma, yüksek
yoğunluklu polietilen (HDPE), polipropilen (PP), Epoksi ve Ecopoxy (BioPoxy 36)
matrislerinde palmiye'nin yanı sıra lif kabağı doğal elyaf kompozitlerinin (NFC)
çekme özellikleri üzerinde, etkiyi dikkate alarak bir araştırmadır. liflerin hacim oranı
(Vf) varyasyonu. Elastik özellikleri tahmin etmek için sonlu eleman analizi, yani tek
yönlü ve doğranmış rastgele fiber yönelimli temsili hacim elemanı (RVE) modelleri
ve analitik simülasyon, yani karışım kuralı (ROM), Halpin-Tsai, Chamis ve Nielsen
yaklaşımları kullanılmıştır. ASTM D3039 standardına göre çekme testi yapılmıştır.
Tasarım uzayını göz önüne alınan parametreler üzerinden tanımlamak ve bu makine
öğrenmesi yaklaşımlarının doğal elyaf kompozitlerinin çekme mukavemetini tahmin
etmedeki güvenilirliğini değerlendirmek için Yapay Sinir Ağı, Çoklu Doğrusal
Regresyon, Uyarlamalı Nöro-Bulanık Çıkarım Sistemi ve Destek Vektör Makinesi
uygulandı. Ayrıca, 0,3 lifli BioPoxy 36 en yüksek gerilme mukavemetini sergiledi.
Sonlu Elemanlar Analizi (finite element analysis) bulguları deneysel sonuçlarla büyük
ölçüde uyumluydu. ANFIS ML aracı, doğal lifli kompozitlerin gerilme mukavemetini
tahmin etmede en az tahmin hatası gösterdi. |
Description: | Doctor of Philosophy in Mechanical Engineering. Institute of Graduate Studies and Research. Thesis (Ph.D.) - Eastern Mediterranean University, Faculty of Engineering, Dept. of Mechanical Engineering, 2021. Co-Supervisor: Assoc. Prof. Dr. Qasim Zeeshan and Supervisor: Asst. Prof. Dr. Babak Safaei |
URI: | http://hdl.handle.net/11129/6460 |
Appears in Collections: | Theses (Master's and Ph.D) – Mechanical Engineering
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