Synergistic artificial neural network scheme for monitoring and diagnosis of multivariate process variation in mean shifts

In quality control, monitoring and diagnosis of multivariate out of control condition is essential in today’s manufacturing industries. The simplest case involves two correlated variables; for instance, monitoring value of temperature and pressure in our environment. Monitoring refers to the ide...

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Main Author:	Marian, Mohd Fairuz
Format:	Thesis
Language:	English English
Published:	2014
Subjects:	TS Manufactures
Online Access:	http://eprints.uthm.edu.my/1540/1/24p%20MOHD%20FAIRUZ%20MARIAN.pdf http://eprints.uthm.edu.my/1540/2/MOHD%20FAIRUZ%20MARIAN%20WATERMARK.pdf
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id	my-uthm-ep.1540
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spelling	my-uthm-ep.15402021-10-03T07:57:51Z Synergistic artificial neural network scheme for monitoring and diagnosis of multivariate process variation in mean shifts 2014-07 Marian, Mohd Fairuz TS Manufactures TS155-194 Production management. Operations management In quality control, monitoring and diagnosis of multivariate out of control condition is essential in today’s manufacturing industries. The simplest case involves two correlated variables; for instance, monitoring value of temperature and pressure in our environment. Monitoring refers to the identification of process condition either it is running in control or out of control. Diagnosis refers to the identification of source variables (X1 and X2) for out of control. In this study, a synergistic artificial neural network scheme was investigated in quality control of process in plastic injection moulding part. This process was selected since it less reported in the literature. In the related point of view, this study should be useful in minimizing the cost of waste materials. The result of this study, suggested this scheme has a superior performance compared to the traditional control chart, namely Multivariate Exponentially Weighted Moving Average (MEWMA). In monitoring, it is effective in rapid detection of out of control without false alarm. In diagnosis, it is able to accurately identify for source of variables. Whereby, diagnosis cannot be performed by traditional control chart. This study is useful for quality control practitioner, particularly in plastic injection moulding industry. 2014-07 Thesis http://eprints.uthm.edu.my/1540/ http://eprints.uthm.edu.my/1540/1/24p%20MOHD%20FAIRUZ%20MARIAN.pdf text en public http://eprints.uthm.edu.my/1540/2/MOHD%20FAIRUZ%20MARIAN%20WATERMARK.pdf text en validuser mphil masters Universiti Tun Hussein Malaysia Fakulti Kejuruteraan Mekanikal dan Pembuatan
institution	Universiti Tun Hussein Onn Malaysia
collection	UTHM Institutional Repository
language	English English
topic	TS Manufactures TS Manufactures
spellingShingle	TS Manufactures TS Manufactures Marian, Mohd Fairuz Synergistic artificial neural network scheme for monitoring and diagnosis of multivariate process variation in mean shifts
description	In quality control, monitoring and diagnosis of multivariate out of control condition is essential in today’s manufacturing industries. The simplest case involves two correlated variables; for instance, monitoring value of temperature and pressure in our environment. Monitoring refers to the identification of process condition either it is running in control or out of control. Diagnosis refers to the identification of source variables (X1 and X2) for out of control. In this study, a synergistic artificial neural network scheme was investigated in quality control of process in plastic injection moulding part. This process was selected since it less reported in the literature. In the related point of view, this study should be useful in minimizing the cost of waste materials. The result of this study, suggested this scheme has a superior performance compared to the traditional control chart, namely Multivariate Exponentially Weighted Moving Average (MEWMA). In monitoring, it is effective in rapid detection of out of control without false alarm. In diagnosis, it is able to accurately identify for source of variables. Whereby, diagnosis cannot be performed by traditional control chart. This study is useful for quality control practitioner, particularly in plastic injection moulding industry.
format	Thesis
qualification_name	Master of Philosophy (M.Phil.)
qualification_level	Master's degree
author	Marian, Mohd Fairuz
author_facet	Marian, Mohd Fairuz
author_sort	Marian, Mohd Fairuz
title	Synergistic artificial neural network scheme for monitoring and diagnosis of multivariate process variation in mean shifts
title_short	Synergistic artificial neural network scheme for monitoring and diagnosis of multivariate process variation in mean shifts
title_full	Synergistic artificial neural network scheme for monitoring and diagnosis of multivariate process variation in mean shifts
title_fullStr	Synergistic artificial neural network scheme for monitoring and diagnosis of multivariate process variation in mean shifts
title_full_unstemmed	Synergistic artificial neural network scheme for monitoring and diagnosis of multivariate process variation in mean shifts
title_sort	synergistic artificial neural network scheme for monitoring and diagnosis of multivariate process variation in mean shifts
granting_institution	Universiti Tun Hussein Malaysia
granting_department	Fakulti Kejuruteraan Mekanikal dan Pembuatan
publishDate	2014
url	http://eprints.uthm.edu.my/1540/1/24p%20MOHD%20FAIRUZ%20MARIAN.pdf http://eprints.uthm.edu.my/1540/2/MOHD%20FAIRUZ%20MARIAN%20WATERMARK.pdf
_version_	1747830813799481344

Synergistic artificial neural network scheme for monitoring and diagnosis of multivariate process variation in mean shifts

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