Multivariate quality control using an integrated artificial neural network scheme: a case study in plastic injection molding industry

Manufacturers using traditional process control charts to monitor their processes often encounter out-of-control signals indicating that the process mean has changed. Most manufacturers are unaware how much these changes in the mean inflate the variance in the process output. In actual, many manu...

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Main Author:	Mohd Khairy, Afiz Azry
Format:	Thesis
Language:	English English English
Published:	2013
Subjects:	TK5101-6720 Telecommunication Including telegraphy, telephone, radio, radar, television
Online Access:	http://eprints.uthm.edu.my/6622/1/24p%20AFIZ%20AZRY%20MOHD%20KHAIRY.pdf http://eprints.uthm.edu.my/6622/2/AFIZ%20AZRY%20MOHD%20KHAIRY%20COPYRIGHT.pdf http://eprints.uthm.edu.my/6622/3/AFIZ%20AZRY%20MOHD%20KHAIRY%20WATERMARK.pdf
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id	my-uthm-ep.6622
record_format	uketd_dc
spelling	my-uthm-ep.66222022-03-10T03:35:33Z Multivariate quality control using an integrated artificial neural network scheme: a case study in plastic injection molding industry 2013-05 Mohd Khairy, Afiz Azry TK5101-6720 Telecommunication. Including telegraphy, telephone, radio, radar, television Manufacturers using traditional process control charts to monitor their processes often encounter out-of-control signals indicating that the process mean has changed. Most manufacturers are unaware how much these changes in the mean inflate the variance in the process output. In actual, many manufacturing processes involve two or more dependent variables and attempting to monitor such variables separately using univariate SPC charting scheme would increase false alarms and leading to wrong decision making The problem becomes more complicated when dealing with small mean shift particularly in identifying the causable variables. In this project study, advances SPC scheme which applied Artificial Neural Networks that were designed to enable balanced monitoring and accurate diagnosis were used. Its performance and effectiveness in actual manufacturing practice were compared with common use traditional process control chart. Thermoplastic Injection Molding was selected in this study since most of the industries applied this method to produce low cost parts. It is important to ensure that good quality parts can be produced according to requirement. The potential benefit from advance SPC schemes was it able to performed rapid detection of process disturbance. However the accuracy of mean shifted diagnosis performance need to improve. 2013-05 Thesis http://eprints.uthm.edu.my/6622/ http://eprints.uthm.edu.my/6622/1/24p%20AFIZ%20AZRY%20MOHD%20KHAIRY.pdf text en public http://eprints.uthm.edu.my/6622/2/AFIZ%20AZRY%20MOHD%20KHAIRY%20COPYRIGHT.pdf text en staffonly http://eprints.uthm.edu.my/6622/3/AFIZ%20AZRY%20MOHD%20KHAIRY%20WATERMARK.pdf text en validuser dphil masters Universiti Tun Hussein Malaysia Fakulti Kejuruteraan Mekanikal dan Pembuatan
institution	Universiti Tun Hussein Onn Malaysia
collection	UTHM Institutional Repository
language	English English English
topic	TK5101-6720 Telecommunication Including telegraphy, telephone, radio, radar, television
spellingShingle	TK5101-6720 Telecommunication Including telegraphy, telephone, radio, radar, television Mohd Khairy, Afiz Azry Multivariate quality control using an integrated artificial neural network scheme: a case study in plastic injection molding industry
description	Manufacturers using traditional process control charts to monitor their processes often encounter out-of-control signals indicating that the process mean has changed. Most manufacturers are unaware how much these changes in the mean inflate the variance in the process output. In actual, many manufacturing processes involve two or more dependent variables and attempting to monitor such variables separately using univariate SPC charting scheme would increase false alarms and leading to wrong decision making The problem becomes more complicated when dealing with small mean shift particularly in identifying the causable variables. In this project study, advances SPC scheme which applied Artificial Neural Networks that were designed to enable balanced monitoring and accurate diagnosis were used. Its performance and effectiveness in actual manufacturing practice were compared with common use traditional process control chart. Thermoplastic Injection Molding was selected in this study since most of the industries applied this method to produce low cost parts. It is important to ensure that good quality parts can be produced according to requirement. The potential benefit from advance SPC schemes was it able to performed rapid detection of process disturbance. However the accuracy of mean shifted diagnosis performance need to improve.
format	Thesis
qualification_name	Doctor of Philosophy (PhD.)
qualification_level	Master's degree
author	Mohd Khairy, Afiz Azry
author_facet	Mohd Khairy, Afiz Azry
author_sort	Mohd Khairy, Afiz Azry
title	Multivariate quality control using an integrated artificial neural network scheme: a case study in plastic injection molding industry
title_short	Multivariate quality control using an integrated artificial neural network scheme: a case study in plastic injection molding industry
title_full	Multivariate quality control using an integrated artificial neural network scheme: a case study in plastic injection molding industry
title_fullStr	Multivariate quality control using an integrated artificial neural network scheme: a case study in plastic injection molding industry
title_full_unstemmed	Multivariate quality control using an integrated artificial neural network scheme: a case study in plastic injection molding industry
title_sort	multivariate quality control using an integrated artificial neural network scheme: a case study in plastic injection molding industry
granting_institution	Universiti Tun Hussein Malaysia
granting_department	Fakulti Kejuruteraan Mekanikal dan Pembuatan
publishDate	2013
url	http://eprints.uthm.edu.my/6622/1/24p%20AFIZ%20AZRY%20MOHD%20KHAIRY.pdf http://eprints.uthm.edu.my/6622/2/AFIZ%20AZRY%20MOHD%20KHAIRY%20COPYRIGHT.pdf http://eprints.uthm.edu.my/6622/3/AFIZ%20AZRY%20MOHD%20KHAIRY%20WATERMARK.pdf
_version_	1747831075749494784

Multivariate quality control using an integrated artificial neural network scheme: a case study in plastic injection molding industry

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