The Graft of ANN-FEM Technique in Macro-mechanics of Multi-oriented Natural Fiber/Polyester Laminates

Low weight and high strength requirements are prime target design objectives in strength demanding applications. Skillful design of low density, low weight and eco-friendly natural fiber composites could provide an alternative material route to the actualization of lighter structures. The present st...

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Main Authors: Christian Emeka Okafor, Christopher Chukwutoo Ihueze
Format: Article
Language:English
Published: Semnan University 2021-04-01
Series:Mechanics of Advanced Composite Structures
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Online Access:https://macs.semnan.ac.ir/article_4966_953a6a4f36f7fecec8d819b2c77e3632.pdf
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author Christian Emeka Okafor
Christopher Chukwutoo Ihueze
author_facet Christian Emeka Okafor
Christopher Chukwutoo Ihueze
author_sort Christian Emeka Okafor
collection DOAJ
description Low weight and high strength requirements are prime target design objectives in strength demanding applications. Skillful design of low density, low weight and eco-friendly natural fiber composites could provide an alternative material route to the actualization of lighter structures. The present study proposed ANN-FEM computational framework for the macro-mechanical analysis of multi-oriented Plantain Empty Fruit Bunch Fiber Laminate (PEFBFL) and Plantain Pseudo Stem Fiber Laminate (PPSFL). Control factors were numerically varied using Finite Element Method (FEM) and the resultant FEM models which encapsulated material properties of the laminate was streamlined into Artificial Neural Network (ANN) training scheme. A standard feed-forward backpropagation network was adopted and the ANN model consists of stacking sequence, laminate aspect ratio and fiber orientation as input variables while the selected network outputs variables include average stress and displacement. The laminate constitutive equation was developed which enabled the establishment of laminate load deformation affiliation and equivalent elastic constants. The damage onset for individual lamina was detected by the maximum principal stress theory and the overall laminate strength of 40.12 N/mm^2 was obtained for PEFBFL and 32.16N/mm^2 for PPSFL. On the whole, there was steady reduction in laminates elastic modulus which points to compromised stiffness in material principal axis arising from gradual failure of the plies, this trend continued until the last ply failure occurred in ply 3 and 4 at 90 degrees in tensile mode of transverse direction. Stresses and displacements observed using CLT agree very closely with predictions of ANN.
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spelling doaj-art-6d562569a3bf4696bb06aefdada75ed62025-08-20T02:49:23ZengSemnan UniversityMechanics of Advanced Composite Structures2423-48262423-70432021-04-0181516810.22075/macs.2021.20579.12634966The Graft of ANN-FEM Technique in Macro-mechanics of Multi-oriented Natural Fiber/Polyester LaminatesChristian Emeka Okafor0Christopher Chukwutoo Ihueze1Department of Mechanical Engineering, Nnamdi Azikiwe University, Awka, NigeriaDepartment of Industrial and Production Engineering, Nnamdi Azikiwe University, Awka, NigeriaLow weight and high strength requirements are prime target design objectives in strength demanding applications. Skillful design of low density, low weight and eco-friendly natural fiber composites could provide an alternative material route to the actualization of lighter structures. The present study proposed ANN-FEM computational framework for the macro-mechanical analysis of multi-oriented Plantain Empty Fruit Bunch Fiber Laminate (PEFBFL) and Plantain Pseudo Stem Fiber Laminate (PPSFL). Control factors were numerically varied using Finite Element Method (FEM) and the resultant FEM models which encapsulated material properties of the laminate was streamlined into Artificial Neural Network (ANN) training scheme. A standard feed-forward backpropagation network was adopted and the ANN model consists of stacking sequence, laminate aspect ratio and fiber orientation as input variables while the selected network outputs variables include average stress and displacement. The laminate constitutive equation was developed which enabled the establishment of laminate load deformation affiliation and equivalent elastic constants. The damage onset for individual lamina was detected by the maximum principal stress theory and the overall laminate strength of 40.12 N/mm^2 was obtained for PEFBFL and 32.16N/mm^2 for PPSFL. On the whole, there was steady reduction in laminates elastic modulus which points to compromised stiffness in material principal axis arising from gradual failure of the plies, this trend continued until the last ply failure occurred in ply 3 and 4 at 90 degrees in tensile mode of transverse direction. Stresses and displacements observed using CLT agree very closely with predictions of ANN.https://macs.semnan.ac.ir/article_4966_953a6a4f36f7fecec8d819b2c77e3632.pdflaminate ply forcesmacro-mechanical analysisplantain fiber compositeartificial neural networkfinite element method
spellingShingle Christian Emeka Okafor
Christopher Chukwutoo Ihueze
The Graft of ANN-FEM Technique in Macro-mechanics of Multi-oriented Natural Fiber/Polyester Laminates
Mechanics of Advanced Composite Structures
laminate ply forces
macro-mechanical analysis
plantain fiber composite
artificial neural network
finite element method
title The Graft of ANN-FEM Technique in Macro-mechanics of Multi-oriented Natural Fiber/Polyester Laminates
title_full The Graft of ANN-FEM Technique in Macro-mechanics of Multi-oriented Natural Fiber/Polyester Laminates
title_fullStr The Graft of ANN-FEM Technique in Macro-mechanics of Multi-oriented Natural Fiber/Polyester Laminates
title_full_unstemmed The Graft of ANN-FEM Technique in Macro-mechanics of Multi-oriented Natural Fiber/Polyester Laminates
title_short The Graft of ANN-FEM Technique in Macro-mechanics of Multi-oriented Natural Fiber/Polyester Laminates
title_sort graft of ann fem technique in macro mechanics of multi oriented natural fiber polyester laminates
topic laminate ply forces
macro-mechanical analysis
plantain fiber composite
artificial neural network
finite element method
url https://macs.semnan.ac.ir/article_4966_953a6a4f36f7fecec8d819b2c77e3632.pdf
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