Pattern recognition for manufacturing process variation using integrated statistical process control – artificial neural network

Variation in manufacturing process is known to be a major source of poor quality products and variation control is essential in quality improvement. In bivariate cases, which involve two correlated quality variables, the traditional statistical process control (SPC) charts are known to be effective...

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Main Author:	Mohd Ariffin, Ahmad Azrizal
Format:	Thesis
Language:	English English English
Published:	2015
Subjects:	TS155-194 Production management Operations management
Online Access:	http://eprints.uthm.edu.my/1279/2/AHMAD%20AZRIZAL%20MOHD%20ARIFFIN%20COPYRIGHT%20DECLARATION.pdf http://eprints.uthm.edu.my/1279/1/24p%20AHMAD%20AZRIZAL%20MOHD%20ARIFFIN.pdf http://eprints.uthm.edu.my/1279/3/AHMAD%20AZRIZAL%20MOHD%20ARIFFIN%20WATERMARK.pdf
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spelling	my-uthm-ep.12792021-09-30T07:02:28Z Pattern recognition for manufacturing process variation using integrated statistical process control – artificial neural network 2015-07 Mohd Ariffin, Ahmad Azrizal TS155-194 Production management. Operations management Variation in manufacturing process is known to be a major source of poor quality products and variation control is essential in quality improvement. In bivariate cases, which involve two correlated quality variables, the traditional statistical process control (SPC) charts are known to be effective in monitoring but they are lack of diagnosis. As such, process monitoring and diagnosis is critical towards continuous quality improvement. This becomes more challenging when involving two correlated variables (bivariate), whereby selection of statistical process control (SPC) scheme becomes more critical. In this research, a scheme to address balanced monitoring and accurate diagnosis was investigated. Investigation has been focused on an integrated SPC - ANN model. This model utilizes the Exponentially Weighted Moving Average (EWMA) control chart and ANN model in two-stage monitoring and diagnosis technique. This scheme was validated in manufacturing of hard disc drive. The study focused on bivariate process for cross correlation function, ρ = 0.3 and 0.7 and mean shifts, μ = ±1.00-2.00 standard deviations. The result of this study, suggested this scheme has a superior performance compared to the traditional control chart. 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 variation. This scheme is effective for cases variations of such loading error, offsetting tool and inconsistent pressure. Therefore, this study should be useful in minimizing the cost of waste materials and has provided a new perspective in realizing balanced monitoring and accurate diagnosis in BQC. 2015-07 Thesis http://eprints.uthm.edu.my/1279/ http://eprints.uthm.edu.my/1279/2/AHMAD%20AZRIZAL%20MOHD%20ARIFFIN%20COPYRIGHT%20DECLARATION.pdf text en staffonly http://eprints.uthm.edu.my/1279/1/24p%20AHMAD%20AZRIZAL%20MOHD%20ARIFFIN.pdf text en public http://eprints.uthm.edu.my/1279/3/AHMAD%20AZRIZAL%20MOHD%20ARIFFIN%20WATERMARK.pdf text en validuser mphil masters Universiti Tun Hussein Onn Malaysia Faculty of Mechanical and Manufacturing Engineering
institution	Universiti Tun Hussein Onn Malaysia
collection	UTHM Institutional Repository
language	English English English
topic	TS155-194 Production management Operations management
spellingShingle	TS155-194 Production management Operations management Mohd Ariffin, Ahmad Azrizal Pattern recognition for manufacturing process variation using integrated statistical process control – artificial neural network
description	Variation in manufacturing process is known to be a major source of poor quality products and variation control is essential in quality improvement. In bivariate cases, which involve two correlated quality variables, the traditional statistical process control (SPC) charts are known to be effective in monitoring but they are lack of diagnosis. As such, process monitoring and diagnosis is critical towards continuous quality improvement. This becomes more challenging when involving two correlated variables (bivariate), whereby selection of statistical process control (SPC) scheme becomes more critical. In this research, a scheme to address balanced monitoring and accurate diagnosis was investigated. Investigation has been focused on an integrated SPC - ANN model. This model utilizes the Exponentially Weighted Moving Average (EWMA) control chart and ANN model in two-stage monitoring and diagnosis technique. This scheme was validated in manufacturing of hard disc drive. The study focused on bivariate process for cross correlation function, ρ = 0.3 and 0.7 and mean shifts, μ = ±1.00-2.00 standard deviations. The result of this study, suggested this scheme has a superior performance compared to the traditional control chart. 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 variation. This scheme is effective for cases variations of such loading error, offsetting tool and inconsistent pressure. Therefore, this study should be useful in minimizing the cost of waste materials and has provided a new perspective in realizing balanced monitoring and accurate diagnosis in BQC.
format	Thesis
qualification_name	Master of Philosophy (M.Phil.)
qualification_level	Master's degree
author	Mohd Ariffin, Ahmad Azrizal
author_facet	Mohd Ariffin, Ahmad Azrizal
author_sort	Mohd Ariffin, Ahmad Azrizal
title	Pattern recognition for manufacturing process variation using integrated statistical process control – artificial neural network
title_short	Pattern recognition for manufacturing process variation using integrated statistical process control – artificial neural network
title_full	Pattern recognition for manufacturing process variation using integrated statistical process control – artificial neural network
title_fullStr	Pattern recognition for manufacturing process variation using integrated statistical process control – artificial neural network
title_full_unstemmed	Pattern recognition for manufacturing process variation using integrated statistical process control – artificial neural network
title_sort	pattern recognition for manufacturing process variation using integrated statistical process control – artificial neural network
granting_institution	Universiti Tun Hussein Onn Malaysia
granting_department	Faculty of Mechanical and Manufacturing Engineering
publishDate	2015
url	http://eprints.uthm.edu.my/1279/2/AHMAD%20AZRIZAL%20MOHD%20ARIFFIN%20COPYRIGHT%20DECLARATION.pdf http://eprints.uthm.edu.my/1279/1/24p%20AHMAD%20AZRIZAL%20MOHD%20ARIFFIN.pdf http://eprints.uthm.edu.my/1279/3/AHMAD%20AZRIZAL%20MOHD%20ARIFFIN%20WATERMARK.pdf
_version_	1747830762368925696

Pattern recognition for manufacturing process variation using integrated statistical process control – artificial neural network

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