Physical and Mechanical Properties Estimation of Ti/HAP Functionally Graded Material Using Artificial Neural Network

This study presents the effort in applying neural network-based system identification techniques by using Back- propagation algorithm to predict somephysical mechanical properties of functionally graded and compositesamples from Ti/HAP, these samples were fabricated by powder metallurgy method at va...

Full description

Bibliographic Details
Main Authors: Jawad K. Oleiwi, Rana A. Anaee, Sura A. Muhsin
Format: Article
Language:English
Published: Unviversity of Technology- Iraq 2016-12-01
Series:Engineering and Technology Journal
Subjects:
Online Access:https://etj.uotechnology.edu.iq/article_120066_a87727dbc7d9df42ad007739857663e9.pdf
_version_ 1797325899535745024
author Jawad K. Oleiwi
Rana A. Anaee
Sura A. Muhsin
author_facet Jawad K. Oleiwi
Rana A. Anaee
Sura A. Muhsin
author_sort Jawad K. Oleiwi
collection DOAJ
description This study presents the effort in applying neural network-based system identification techniques by using Back- propagation algorithm to predict somephysical mechanical properties of functionally graded and compositesamples from Ti/HAP, these samples were fabricated by powder metallurgy method at various volume fraction of hydroxyapatite and at n equal (0.8, 1, and 1.2). Because of important of advanced materials such as FGMs as alternative industrial material, it is necessary to measure the physical properties of these materials such as porosity, density, hardness, compression …etc. Therefore the ANN will be used to estimate these properties and give a good performance to the network.
first_indexed 2024-03-08T06:15:58Z
format Article
id doaj.art-e1e67697a9dc492ca93a39fa90bfe9f9
institution Directory Open Access Journal
issn 1681-6900
2412-0758
language English
last_indexed 2024-03-08T06:15:58Z
publishDate 2016-12-01
publisher Unviversity of Technology- Iraq
record_format Article
series Engineering and Technology Journal
spelling doaj.art-e1e67697a9dc492ca93a39fa90bfe9f92024-02-04T17:27:58ZengUnviversity of Technology- IraqEngineering and Technology Journal1681-69002412-07582016-12-013412A2174218010.30684/etj.34.12A.1120066Physical and Mechanical Properties Estimation of Ti/HAP Functionally Graded Material Using Artificial Neural NetworkJawad K. OleiwiRana A. AnaeeSura A. MuhsinThis study presents the effort in applying neural network-based system identification techniques by using Back- propagation algorithm to predict somephysical mechanical properties of functionally graded and compositesamples from Ti/HAP, these samples were fabricated by powder metallurgy method at various volume fraction of hydroxyapatite and at n equal (0.8, 1, and 1.2). Because of important of advanced materials such as FGMs as alternative industrial material, it is necessary to measure the physical properties of these materials such as porosity, density, hardness, compression …etc. Therefore the ANN will be used to estimate these properties and give a good performance to the network.https://etj.uotechnology.edu.iq/article_120066_a87727dbc7d9df42ad007739857663e9.pdfhapannfgmphysical properties
spellingShingle Jawad K. Oleiwi
Rana A. Anaee
Sura A. Muhsin
Physical and Mechanical Properties Estimation of Ti/HAP Functionally Graded Material Using Artificial Neural Network
Engineering and Technology Journal
hap
ann
fgm
physical properties
title Physical and Mechanical Properties Estimation of Ti/HAP Functionally Graded Material Using Artificial Neural Network
title_full Physical and Mechanical Properties Estimation of Ti/HAP Functionally Graded Material Using Artificial Neural Network
title_fullStr Physical and Mechanical Properties Estimation of Ti/HAP Functionally Graded Material Using Artificial Neural Network
title_full_unstemmed Physical and Mechanical Properties Estimation of Ti/HAP Functionally Graded Material Using Artificial Neural Network
title_short Physical and Mechanical Properties Estimation of Ti/HAP Functionally Graded Material Using Artificial Neural Network
title_sort physical and mechanical properties estimation of ti hap functionally graded material using artificial neural network
topic hap
ann
fgm
physical properties
url https://etj.uotechnology.edu.iq/article_120066_a87727dbc7d9df42ad007739857663e9.pdf
work_keys_str_mv AT jawadkoleiwi physicalandmechanicalpropertiesestimationoftihapfunctionallygradedmaterialusingartificialneuralnetwork
AT ranaaanaee physicalandmechanicalpropertiesestimationoftihapfunctionallygradedmaterialusingartificialneuralnetwork
AT suraamuhsin physicalandmechanicalpropertiesestimationoftihapfunctionallygradedmaterialusingartificialneuralnetwork