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...
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Format: | Article |
Language: | English |
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Unviversity of Technology- Iraq
2016-12-01
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Series: | Engineering and Technology Journal |
Subjects: | |
Online Access: | https://etj.uotechnology.edu.iq/article_120066_a87727dbc7d9df42ad007739857663e9.pdf |
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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 |