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

Abdelrahman El-Sawy; Anwar P. P. Abdul Majeed; Rabiu Muazu Musa; Mohd Azraai Mohd Razman; Mohd Hasnun Arif Hassan; Abdul Aziz Jaafar

doi:10.1007/978-981-13-8323-6_34

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

Abdelrahman El-Sawy, Anwar P. P. Abdul Majeed^*, Rabiu Muazu Musa, Mohd Azraai Mohd Razman, Mohd Hasnun Arif Hassan, Abdul Aziz Jaafar

^*Corresponding author for this work

Research output: Chapter in Book or Report/Conference proceeding › Chapter › peer-review

1 Citation (Scopus)

Abstract

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.

Original language	English
Title of host publication	Lecture Notes in Mechanical Engineering
Publisher	Pleiades Publishing
Pages	403-407
Number of pages	5
DOIs	https://doi.org/10.1007/978-981-13-8323-6_34
Publication status	Published - 2020
Externally published	Yes

Publication series

Name	Lecture Notes in Mechanical Engineering
ISSN (Print)	2195-4356
ISSN (Electronic)	2195-4364

Keywords

Artificial neural network
Composite
Flexural strength

Access to Document

10.1007/978-981-13-8323-6_34

Cite this

El-Sawy, A., P. P. Abdul Majeed, A., Musa, R. M., Mohd Razman, M. A., Hassan, M. H. A., & Jaafar, A. A. (2020). The Flexural Strength Prediction of Porous Cu-Sn-Ti Composites via Artificial Neural Networks. In Lecture Notes in Mechanical Engineering (pp. 403-407). (Lecture Notes in Mechanical Engineering). Pleiades Publishing. https://doi.org/10.1007/978-981-13-8323-6_34

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abstract = "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.",

keywords = "Artificial neural network, Composite, Flexural strength",

author = "Abdelrahman El-Sawy and {P. P. Abdul Majeed}, Anwar and Musa, {Rabiu Muazu} and {Mohd Razman}, {Mohd Azraai} and Hassan, {Mohd Hasnun Arif} and Jaafar, {Abdul Aziz}",

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doi = "10.1007/978-981-13-8323-6_34",

language = "English",

series = "Lecture Notes in Mechanical Engineering",

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The Flexural Strength Prediction of Porous Cu-Sn-Ti Composites via Artificial Neural Networks. / El-Sawy, Abdelrahman; P. P. Abdul Majeed, Anwar; Musa, Rabiu Muazu et al.
Lecture Notes in Mechanical Engineering. Pleiades Publishing, 2020. p. 403-407 (Lecture Notes in Mechanical Engineering).

Research output: Chapter in Book or Report/Conference proceeding › Chapter › peer-review

TY - CHAP

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AU - El-Sawy, Abdelrahman

AU - P. P. Abdul Majeed, Anwar

AU - Musa, Rabiu Muazu

AU - Mohd Razman, Mohd Azraai

AU - Hassan, Mohd Hasnun Arif

AU - Jaafar, Abdul Aziz

PY - 2020

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N2 - 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.

AB - 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.

KW - Artificial neural network

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KW - Flexural strength

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

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