Computational intelligence approach for prediction of hardness performance in coating process

Nowadays, coated materials are widely used due to their excellent properties especially for the hardness performance. The hardness of coated tools is determined by the coating process parameters. Traditionally, optimization to obtain the best coating performance of the parameters in a coating proces...

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Main Author:	Mohamad, Muhammad ‘Arif
Format:	Thesis
Language:	English
Published:	2014
Subjects:	QA75 Electronic computers Computer science
Online Access:	http://eprints.utm.my/id/eprint/48024/25/Muhammad%27ArifMohamadMFC2014.pdf
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id	my-utm-ep.48024
record_format	uketd_dc
spelling	my-utm-ep.480242017-08-06T09:54:58Z Computational intelligence approach for prediction of hardness performance in coating process 2014-06 Mohamad, Muhammad ‘Arif QA75 Electronic computers. Computer science Nowadays, coated materials are widely used due to their excellent properties especially for the hardness performance. The hardness of coated tools is determined by the coating process parameters. Traditionally, optimization to obtain the best coating performance of the parameters in a coating process was done by trial and error approach. However the traditional approach has raised issues with regards to cost and customization. In this research, these two issues were addressed by using a computational intelligence approach to develop a model for predicting the output responses in order to identify the optimal parameters used in coating process. Previous studies have shown that this approach was successfully adopted for optimization purpose in many types of domains. However, it was not yet applied in the coating process domain. Thus, two methods from computational intelligence approach were applied, namely Support Vector Machine (SVM) and Artificial Neural Network (ANN). The comparisons of the performances of the developed models were conducted based on predictive performance measurements such as percentage error, mean squared error (MSE), co-efficient determination (R2), and model accuracy and complexity. The results showed that, SVM obtained better predictive performances and less complicated in comparison to other prediction models. As a conclusion, SVM has demonstrated its capability in predicting the hardness performance of coating process and outperformed the other models. Besides that, the model is a promising alternative tool for coating process optimization as compared to the traditional approach. 2014-06 Thesis http://eprints.utm.my/id/eprint/48024/ http://eprints.utm.my/id/eprint/48024/25/Muhammad%27ArifMohamadMFC2014.pdf application/pdf en public masters Universiti Teknologi Malaysia, Faculty of Computing Faculty of Computing
institution	Universiti Teknologi Malaysia
collection	UTM Institutional Repository
language	English
topic	QA75 Electronic computers Computer science
spellingShingle	QA75 Electronic computers Computer science Mohamad, Muhammad ‘Arif Computational intelligence approach for prediction of hardness performance in coating process
description	Nowadays, coated materials are widely used due to their excellent properties especially for the hardness performance. The hardness of coated tools is determined by the coating process parameters. Traditionally, optimization to obtain the best coating performance of the parameters in a coating process was done by trial and error approach. However the traditional approach has raised issues with regards to cost and customization. In this research, these two issues were addressed by using a computational intelligence approach to develop a model for predicting the output responses in order to identify the optimal parameters used in coating process. Previous studies have shown that this approach was successfully adopted for optimization purpose in many types of domains. However, it was not yet applied in the coating process domain. Thus, two methods from computational intelligence approach were applied, namely Support Vector Machine (SVM) and Artificial Neural Network (ANN). The comparisons of the performances of the developed models were conducted based on predictive performance measurements such as percentage error, mean squared error (MSE), co-efficient determination (R2), and model accuracy and complexity. The results showed that, SVM obtained better predictive performances and less complicated in comparison to other prediction models. As a conclusion, SVM has demonstrated its capability in predicting the hardness performance of coating process and outperformed the other models. Besides that, the model is a promising alternative tool for coating process optimization as compared to the traditional approach.
format	Thesis
qualification_level	Master's degree
author	Mohamad, Muhammad ‘Arif
author_facet	Mohamad, Muhammad ‘Arif
author_sort	Mohamad, Muhammad ‘Arif
title	Computational intelligence approach for prediction of hardness performance in coating process
title_short	Computational intelligence approach for prediction of hardness performance in coating process
title_full	Computational intelligence approach for prediction of hardness performance in coating process
title_fullStr	Computational intelligence approach for prediction of hardness performance in coating process
title_full_unstemmed	Computational intelligence approach for prediction of hardness performance in coating process
title_sort	computational intelligence approach for prediction of hardness performance in coating process
granting_institution	Universiti Teknologi Malaysia, Faculty of Computing
granting_department	Faculty of Computing
publishDate	2014
url	http://eprints.utm.my/id/eprint/48024/25/Muhammad%27ArifMohamadMFC2014.pdf
_version_	1747817289253650432

Computational intelligence approach for prediction of hardness performance in coating process

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