Design optimization of ann-based pattern recognizer for multivariate quality control

In manufacturing industries, process variation is known to be major source of poor quality. As such, process monitoring and diagnosis is critical towards continuous quality improvement. This becomes more challenging when involving two or more correlated variables or known as multivariate. Proc...

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Main Author:	Abdul Jamil, Muhamad Faizal
Format:	Thesis
Language:	English English English
Published:	2013
Subjects:	TS Manufactures
Online Access:	http://eprints.uthm.edu.my/6705/1/24p%20MUHAMAD%20FAIZAL%20ABDUL%20JAMIL.pdf http://eprints.uthm.edu.my/6705/2/MUHAMAD%20FAIZAL%20ABDUL%20JAMIL%20COPYRIGHT%20DECLARATION.pdf http://eprints.uthm.edu.my/6705/3/MUHAMAD%20FAIZAL%20ABDUL%20JAMIL%20WATERMARK.pdf
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id	my-uthm-ep.6705
record_format	uketd_dc
spelling	my-uthm-ep.67052022-03-14T02:13:15Z Design optimization of ann-based pattern recognizer for multivariate quality control 2013-05 Abdul Jamil, Muhamad Faizal TS Manufactures TS155-194 Production management. Operations management In manufacturing industries, process variation is known to be major source of poor quality. As such, process monitoring and diagnosis is critical towards continuous quality improvement. This becomes more challenging when involving two or more correlated variables or known as multivariate. Process monitoring refers to the identification of process status either it is running within a statistically in-control or out-of-control condition, while process diagnosis refers to the identification of the source variables of out-of-control process. The traditional statistical process control (SPC) charting scheme are known to be effective in monitoring aspects, but they are lack of diagnosis. In recent years, the artificial neural network (ANN) based pattern recognition schemes has been developed for solving this issue. The existing ANN model recognizers are mainly utilize raw data as input representation, which resulted in limited performance. In order to improve the monitoring-diagnosis capability, in this research, the feature based input representation shall be investigated using empirical method in designing the ANN model recognizer. 2013-05 Thesis http://eprints.uthm.edu.my/6705/ http://eprints.uthm.edu.my/6705/1/24p%20MUHAMAD%20FAIZAL%20ABDUL%20JAMIL.pdf text en public http://eprints.uthm.edu.my/6705/2/MUHAMAD%20FAIZAL%20ABDUL%20JAMIL%20COPYRIGHT%20DECLARATION.pdf text en staffonly http://eprints.uthm.edu.my/6705/3/MUHAMAD%20FAIZAL%20ABDUL%20JAMIL%20WATERMARK.pdf text en validuser mphil masters Universiti Tun Hussein Onn Malaysia Fakulti Kejuruteraan Mekanikal dan Pembuatan
institution	Universiti Tun Hussein Onn Malaysia
collection	UTHM Institutional Repository
language	English English English
topic	TS Manufactures TS Manufactures
spellingShingle	TS Manufactures TS Manufactures Abdul Jamil, Muhamad Faizal Design optimization of ann-based pattern recognizer for multivariate quality control
description	In manufacturing industries, process variation is known to be major source of poor quality. As such, process monitoring and diagnosis is critical towards continuous quality improvement. This becomes more challenging when involving two or more correlated variables or known as multivariate. Process monitoring refers to the identification of process status either it is running within a statistically in-control or out-of-control condition, while process diagnosis refers to the identification of the source variables of out-of-control process. The traditional statistical process control (SPC) charting scheme are known to be effective in monitoring aspects, but they are lack of diagnosis. In recent years, the artificial neural network (ANN) based pattern recognition schemes has been developed for solving this issue. The existing ANN model recognizers are mainly utilize raw data as input representation, which resulted in limited performance. In order to improve the monitoring-diagnosis capability, in this research, the feature based input representation shall be investigated using empirical method in designing the ANN model recognizer.
format	Thesis
qualification_name	Master of Philosophy (M.Phil.)
qualification_level	Master's degree
author	Abdul Jamil, Muhamad Faizal
author_facet	Abdul Jamil, Muhamad Faizal
author_sort	Abdul Jamil, Muhamad Faizal
title	Design optimization of ann-based pattern recognizer for multivariate quality control
title_short	Design optimization of ann-based pattern recognizer for multivariate quality control
title_full	Design optimization of ann-based pattern recognizer for multivariate quality control
title_fullStr	Design optimization of ann-based pattern recognizer for multivariate quality control
title_full_unstemmed	Design optimization of ann-based pattern recognizer for multivariate quality control
title_sort	design optimization of ann-based pattern recognizer for multivariate quality control
granting_institution	Universiti Tun Hussein Onn Malaysia
granting_department	Fakulti Kejuruteraan Mekanikal dan Pembuatan
publishDate	2013
url	http://eprints.uthm.edu.my/6705/1/24p%20MUHAMAD%20FAIZAL%20ABDUL%20JAMIL.pdf http://eprints.uthm.edu.my/6705/2/MUHAMAD%20FAIZAL%20ABDUL%20JAMIL%20COPYRIGHT%20DECLARATION.pdf http://eprints.uthm.edu.my/6705/3/MUHAMAD%20FAIZAL%20ABDUL%20JAMIL%20WATERMARK.pdf
_version_	1747831085051412480

Design optimization of ann-based pattern recognizer for multivariate quality control

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