A comparison study of failure prediction of composite laminate using finite element analysis and artificial neural network / Muhammad Nadiarulah Nanihar

Composite material failure has been studied extensively for many years. However, the failure behaviour of composite materials that spontaneously fail is challenging to analyse. Hybrid composite is one of the recommended methods in modern construction to improve composites unexpected failure mode. Ho...

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Main Author:	Nanihar, Muhammad Nadiarulah
Format:	Thesis
Language:	English
Published:	2021
Subjects:	Composite materials TJ Mechanical engineering and machinery
Online Access:	https://ir.uitm.edu.my/id/eprint/60254/1/60254.pdf
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id	my-uitm-ir.60254
record_format	uketd_dc
spelling	my-uitm-ir.602542022-05-24T08:19:55Z A comparison study of failure prediction of composite laminate using finite element analysis and artificial neural network / Muhammad Nadiarulah Nanihar 2021-09 Nanihar, Muhammad Nadiarulah Composite materials TJ Mechanical engineering and machinery Composite material failure has been studied extensively for many years. However, the failure behaviour of composite materials that spontaneously fail is challenging to analyse. Hybrid composite is one of the recommended methods in modern construction to improve composites unexpected failure mode. However, because failure behaviour of hybrid composite laminates demands a lot of data, it must be predicted properly. This study's purpose is to predict the failure of hybrid composite laminates under uniaxial tension using artificial neural networks. Changing the angle of fibre orientation changed the failure behaviour of composite laminates. ANSYS Mechanical APDL 2020 was used to construct FE models to simulate physical testing. It was predicted using the ANSYS standard formulation and Maximum Stress Failure Criteria. On had already checked the model's results against acceptable public results. Using simply supported composite plates, uniaxial tension was studied. The plate has 24 layers and a layup of [0,0]. The FPF load represents the load required to attain the study's failure condition. Angle fibre orientation may affect the failure mode of composite laminates in uniaxial stress. The ANN tool in MATLAB predicted the failure. Finally, the ANSYS APDL data failure analysis was compared to the ANN model failure data. For Glass/Epoxy, Graphite/Epoxy, and Boron/Epoxy, the output failure rates are 18.97%, 6.25%, and 4.04 percent, respectively. The results of the study revealed that the method approaches produced more realistic and reliable results, with FEA results closely matching the analytical results. The current study is notable in that it advances knowledge about predicting failure behaviour in hybrid composite laminates using artificial neural networks. 2021-09 Thesis https://ir.uitm.edu.my/id/eprint/60254/ https://ir.uitm.edu.my/id/eprint/60254/1/60254.pdf text en public masters Universiti Teknologi MARA College of Engineering Mahmud, Jamaluddin (Professor Ir. Dr.)
institution	Universiti Teknologi MARA
collection	UiTM Institutional Repository
language	English
advisor	Mahmud, Jamaluddin (Professor Ir. Dr.)
topic	Composite materials TJ Mechanical engineering and machinery
spellingShingle	Composite materials TJ Mechanical engineering and machinery Nanihar, Muhammad Nadiarulah A comparison study of failure prediction of composite laminate using finite element analysis and artificial neural network / Muhammad Nadiarulah Nanihar
description	Composite material failure has been studied extensively for many years. However, the failure behaviour of composite materials that spontaneously fail is challenging to analyse. Hybrid composite is one of the recommended methods in modern construction to improve composites unexpected failure mode. However, because failure behaviour of hybrid composite laminates demands a lot of data, it must be predicted properly. This study's purpose is to predict the failure of hybrid composite laminates under uniaxial tension using artificial neural networks. Changing the angle of fibre orientation changed the failure behaviour of composite laminates. ANSYS Mechanical APDL 2020 was used to construct FE models to simulate physical testing. It was predicted using the ANSYS standard formulation and Maximum Stress Failure Criteria. On had already checked the model's results against acceptable public results. Using simply supported composite plates, uniaxial tension was studied. The plate has 24 layers and a layup of [0,0]. The FPF load represents the load required to attain the study's failure condition. Angle fibre orientation may affect the failure mode of composite laminates in uniaxial stress. The ANN tool in MATLAB predicted the failure. Finally, the ANSYS APDL data failure analysis was compared to the ANN model failure data. For Glass/Epoxy, Graphite/Epoxy, and Boron/Epoxy, the output failure rates are 18.97%, 6.25%, and 4.04 percent, respectively. The results of the study revealed that the method approaches produced more realistic and reliable results, with FEA results closely matching the analytical results. The current study is notable in that it advances knowledge about predicting failure behaviour in hybrid composite laminates using artificial neural networks.
format	Thesis
qualification_level	Master's degree
author	Nanihar, Muhammad Nadiarulah
author_facet	Nanihar, Muhammad Nadiarulah
author_sort	Nanihar, Muhammad Nadiarulah
title	A comparison study of failure prediction of composite laminate using finite element analysis and artificial neural network / Muhammad Nadiarulah Nanihar
title_short	A comparison study of failure prediction of composite laminate using finite element analysis and artificial neural network / Muhammad Nadiarulah Nanihar
title_full	A comparison study of failure prediction of composite laminate using finite element analysis and artificial neural network / Muhammad Nadiarulah Nanihar
title_fullStr	A comparison study of failure prediction of composite laminate using finite element analysis and artificial neural network / Muhammad Nadiarulah Nanihar
title_full_unstemmed	A comparison study of failure prediction of composite laminate using finite element analysis and artificial neural network / Muhammad Nadiarulah Nanihar
title_sort	comparison study of failure prediction of composite laminate using finite element analysis and artificial neural network / muhammad nadiarulah nanihar
granting_institution	Universiti Teknologi MARA
granting_department	College of Engineering
publishDate	2021
url	https://ir.uitm.edu.my/id/eprint/60254/1/60254.pdf
_version_	1783735105181712384

A comparison study of failure prediction of composite laminate using finite element analysis and artificial neural network / Muhammad Nadiarulah Nanihar

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