The Flexural Strength Prediction of Porous Cu-Sn-Ti Composites via Artificial Neural Networks

Porous alloy-composites have demonstrated excellent qualities with regards to grinding superalloys. Flexural strength is an important mechanical property associated with the porosity level as well as inhomogeneity in porous composites. Owing to the non-linear characteristics of the constituents of t...

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Main Authors:	El Sawy, Abdelrahman, Anwar, P. P. Abdul Majeed, Musa, Rabiu Muazu, Mohd Azraai, M. Razman, Mohd Hasnun Ariff, Hassan, Abdul Aziz, Jaafar
Format:	Book Section
Language:	English English English
Published:	Universiti Malaysia Pahang 2018
Subjects:	TS Manufactures
Online Access:	http://umpir.ump.edu.my/id/eprint/24528/ http://umpir.ump.edu.my/id/eprint/24528/ http://umpir.ump.edu.my/id/eprint/24528/ http://umpir.ump.edu.my/id/eprint/24528/1/62.%20The%20flexural%20strength%20prediction%20of%20porous%20cu-sn-ti.pdf http://umpir.ump.edu.my/id/eprint/24528/8/8.%20The%20flexural%20strength%20prediction%20of%20porous%20Cu-Sn-Ti%20composites%20via%20artificial%20neural%20networks.pdf http://umpir.ump.edu.my/id/eprint/24528/9/8.1%20The%20flexural%20strength%20prediction%20of%20porous%20Cu-Sn-Ti%20composites%20via%20artificial%20neural%20networks.pdf

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recordtype	eprints
spelling	ump-245282020-02-11T07:10:39Z http://umpir.ump.edu.my/id/eprint/24528/ The Flexural Strength Prediction of Porous Cu-Sn-Ti Composites via Artificial Neural Networks El Sawy, Abdelrahman Anwar, P. P. Abdul Majeed Musa, Rabiu Muazu Mohd Azraai, M. Razman Mohd Hasnun Ariff, Hassan Abdul Aziz, Jaafar TS Manufactures Porous alloy-composites have demonstrated excellent qualities with regards to grinding superalloys. Flexural strength is an important mechanical property associated with the porosity level as well as inhomogeneity in porous composites. Owing to the non-linear characteristics of the constituents of the composite material, the prediction of specific mechanical properties by means of the conventional regression model is often unsatisfactory. Therefore, the utilisation of artificial intelligence for the prediction of such properties is non-trivial. This study evaluates the efficacy of artificial neural network (ANN) in predicting the flexural strength of porous Cu-Sn-Ti composite with Molybdenum disulfide (MoS2) particles. The input parameters of the ANN model are the average carbamide particles size, the porosity volume as well as the weight fraction of the MoS2 particles. The determination of the number of hidden neurons of the single hidden layer ANN model developed is obtained via an empirical formulation. The ANN model developed is compared to a conventional multiple linear regression (MLR) model. It was demonstrated that the ANN-based model is able to predict well the flexural strength of the porous-composite investigated in comparison to the MLR model. Universiti Malaysia Pahang 2018-11 Book Section PeerReviewed pdf en http://umpir.ump.edu.my/id/eprint/24528/1/62.%20The%20flexural%20strength%20prediction%20of%20porous%20cu-sn-ti.pdf pdf en http://umpir.ump.edu.my/id/eprint/24528/8/8.%20The%20flexural%20strength%20prediction%20of%20porous%20Cu-Sn-Ti%20composites%20via%20artificial%20neural%20networks.pdf pdf en http://umpir.ump.edu.my/id/eprint/24528/9/8.1%20The%20flexural%20strength%20prediction%20of%20porous%20Cu-Sn-Ti%20composites%20via%20artificial%20neural%20networks.pdf El Sawy, Abdelrahman and Anwar, P. P. Abdul Majeed and Musa, Rabiu Muazu and Mohd Azraai, M. Razman and Mohd Hasnun Ariff, Hassan and Abdul Aziz, Jaafar (2018) The Flexural Strength Prediction of Porous Cu-Sn-Ti Composites via Artificial Neural Networks. In: Lecture Notes in Mechanical Engineering. Universiti Malaysia Pahang, pp. 403-407. ISBN 978-981-13-8323-6 https://link.springer.com/chapter/10.1007/978-981-13-8323-6_34 https://doi.org/10.1007/978-981-13-8323-6_34
repository_type	Digital Repository
institution_category	Local University
institution	Universiti Malaysia Pahang
building	UMP Institutional Repository
collection	Online Access
language	English English English
topic	TS Manufactures
spellingShingle	TS Manufactures El Sawy, Abdelrahman Anwar, P. P. Abdul Majeed Musa, Rabiu Muazu Mohd Azraai, M. Razman Mohd Hasnun Ariff, Hassan Abdul Aziz, Jaafar The Flexural Strength Prediction of Porous Cu-Sn-Ti Composites via Artificial Neural Networks
description	Porous alloy-composites have demonstrated excellent qualities with regards to grinding superalloys. Flexural strength is an important mechanical property associated with the porosity level as well as inhomogeneity in porous composites. Owing to the non-linear characteristics of the constituents of the composite material, the prediction of specific mechanical properties by means of the conventional regression model is often unsatisfactory. Therefore, the utilisation of artificial intelligence for the prediction of such properties is non-trivial. This study evaluates the efficacy of artificial neural network (ANN) in predicting the flexural strength of porous Cu-Sn-Ti composite with Molybdenum disulfide (MoS2) particles. The input parameters of the ANN model are the average carbamide particles size, the porosity volume as well as the weight fraction of the MoS2 particles. The determination of the number of hidden neurons of the single hidden layer ANN model developed is obtained via an empirical formulation. The ANN model developed is compared to a conventional multiple linear regression (MLR) model. It was demonstrated that the ANN-based model is able to predict well the flexural strength of the porous-composite investigated in comparison to the MLR model.
format	Book Section
author	El Sawy, Abdelrahman Anwar, P. P. Abdul Majeed Musa, Rabiu Muazu Mohd Azraai, M. Razman Mohd Hasnun Ariff, Hassan Abdul Aziz, Jaafar
author_facet	El Sawy, Abdelrahman Anwar, P. P. Abdul Majeed Musa, Rabiu Muazu Mohd Azraai, M. Razman Mohd Hasnun Ariff, Hassan Abdul Aziz, Jaafar
author_sort	El Sawy, Abdelrahman
title	The Flexural Strength Prediction of Porous Cu-Sn-Ti Composites via Artificial Neural Networks
title_short	The Flexural Strength Prediction of Porous Cu-Sn-Ti Composites via Artificial Neural Networks
title_full	The Flexural Strength Prediction of Porous Cu-Sn-Ti Composites via Artificial Neural Networks
title_fullStr	The Flexural Strength Prediction of Porous Cu-Sn-Ti Composites via Artificial Neural Networks
title_full_unstemmed	The Flexural Strength Prediction of Porous Cu-Sn-Ti Composites via Artificial Neural Networks
title_sort	flexural strength prediction of porous cu-sn-ti composites via artificial neural networks
publisher	Universiti Malaysia Pahang
publishDate	2018
url	http://umpir.ump.edu.my/id/eprint/24528/ http://umpir.ump.edu.my/id/eprint/24528/ http://umpir.ump.edu.my/id/eprint/24528/ http://umpir.ump.edu.my/id/eprint/24528/1/62.%20The%20flexural%20strength%20prediction%20of%20porous%20cu-sn-ti.pdf http://umpir.ump.edu.my/id/eprint/24528/8/8.%20The%20flexural%20strength%20prediction%20of%20porous%20Cu-Sn-Ti%20composites%20via%20artificial%20neural%20networks.pdf http://umpir.ump.edu.my/id/eprint/24528/9/8.1%20The%20flexural%20strength%20prediction%20of%20porous%20Cu-Sn-Ti%20composites%20via%20artificial%20neural%20networks.pdf
first_indexed	2023-09-18T22:37:11Z
last_indexed	2023-09-18T22:37:11Z
_version_	1777416673509244928

The Flexural Strength Prediction of Porous Cu-Sn-Ti Composites via Artificial Neural Networks

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