Cover Image

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...

Full description

Saved in:
Bibliographic Details
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/24528/1/62.%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
http://umpir.ump.edu.my/id/eprint/24528/
https://link.springer.com/chapter/10.1007/978-981-13-8323-6_34
https://doi.org/10.1007/978-981-13-8323-6_34
Tags: Add Tag
No Tags, Be the first to tag this record!
id my.ump.umpir.24528
record_format eprints
spelling my.ump.umpir.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
institution Universiti Malaysia Pahang
building UMP Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Malaysia Pahang
content_source UMP Institutional Repository
url_provider http://umpir.ump.edu.my/
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/24528/1/62.%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
http://umpir.ump.edu.my/id/eprint/24528/
https://link.springer.com/chapter/10.1007/978-981-13-8323-6_34
https://doi.org/10.1007/978-981-13-8323-6_34
_version_ 1662754714041712640
score 13.211869

Similar Items