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Please use this identifier to cite or link to this item: http://hdl.handle.net/11129/6460

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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