Design optimization for the two-stage bivariate pattern recognition scheme

In manufacturing operations, unnatural process variation has become a major contributor to a poor quality product. Therefore, monitoring and diagnosis of variation is critical in quality control. Monitoring refers to the identification of process condition either it is running within in statisticall...

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Main Author:	Mokhtar, Mohd Shukri
Format:	Thesis
Language:	English English English
Published:	2015
Subjects:	TS155-194 Production management Operations management
Online Access:	http://eprints.uthm.edu.my/1380/2/MOHD%20SHUKRI%20MOKHTAR%20COPYRIGHT%20DECLARATION.pdf http://eprints.uthm.edu.my/1380/1/24p%20MOHD%20SHUKRI%20MOKHTAR.pdf http://eprints.uthm.edu.my/1380/3/MOHD%20SHUKRI%20MOKHTAR%20WATERMARK.pdf
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spelling	my-uthm-ep.13802021-10-03T06:37:00Z Design optimization for the two-stage bivariate pattern recognition scheme 2015-06 Mokhtar, Mohd Shukri TS155-194 Production management. Operations management In manufacturing operations, unnatural process variation has become a major contributor to a poor quality product. Therefore, monitoring and diagnosis of variation is critical in quality control. Monitoring refers to the identification of process condition either it is running within in statistically in-control or out-of-control, whereas diagnosis refers to the identification of the source of out-of-control process. Selection of SPC scheme becomes more challenging when involving two correlated variables, which are known as bivariate quality control (BQC). Generally, the traditional SPC charting schemes were known to be effective in monitoring aspects, but there were unable to provide information towards diagnosis. In order to overcome this issue, many researches proposed an artificial neural network (ANN) - based pattern recognition schemes. Such schemes were mainly utilize raw data as input representation into an ANN recognizer, which resulted in limited performance. In this research, an integrated MEWMA-ANN scheme was investigated. The optimal design parameters for the MEWMA control chart have been studied. The study focused on BQC with variation in mean shifts (μ = ±0.75 ~ 3.00) standard deviations and cross correlation function (ρ = 0.1 ~ 0.9). The monitoring and diagnosis performances were evaluated based on the average run length (ARL0, ARL1) and recognition accuracy (RA) respectively. The selected optimal design parameters with λ=0.10, H=8.64 gave better performance among the other designs, namely, average run length, ARL1=3.24 ~ 16.93 (for out-of-control process) and recognition accuracy, RA=89.05 ~ 97.73%. For in-control process, design parameters with λ=0.40, H=10.31 parameter gave superior performance with ARL0 = 676.81 ~ 921.71, which is more effective in avoiding false alarm with any correlation. 2015-06 Thesis http://eprints.uthm.edu.my/1380/ http://eprints.uthm.edu.my/1380/2/MOHD%20SHUKRI%20MOKHTAR%20COPYRIGHT%20DECLARATION.pdf text en staffonly http://eprints.uthm.edu.my/1380/1/24p%20MOHD%20SHUKRI%20MOKHTAR.pdf text en public http://eprints.uthm.edu.my/1380/3/MOHD%20SHUKRI%20MOKHTAR%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 Mokhtar, Mohd Shukri Design optimization for the two-stage bivariate pattern recognition scheme
description	In manufacturing operations, unnatural process variation has become a major contributor to a poor quality product. Therefore, monitoring and diagnosis of variation is critical in quality control. Monitoring refers to the identification of process condition either it is running within in statistically in-control or out-of-control, whereas diagnosis refers to the identification of the source of out-of-control process. Selection of SPC scheme becomes more challenging when involving two correlated variables, which are known as bivariate quality control (BQC). Generally, the traditional SPC charting schemes were known to be effective in monitoring aspects, but there were unable to provide information towards diagnosis. In order to overcome this issue, many researches proposed an artificial neural network (ANN) - based pattern recognition schemes. Such schemes were mainly utilize raw data as input representation into an ANN recognizer, which resulted in limited performance. In this research, an integrated MEWMA-ANN scheme was investigated. The optimal design parameters for the MEWMA control chart have been studied. The study focused on BQC with variation in mean shifts (μ = ±0.75 ~ 3.00) standard deviations and cross correlation function (ρ = 0.1 ~ 0.9). The monitoring and diagnosis performances were evaluated based on the average run length (ARL0, ARL1) and recognition accuracy (RA) respectively. The selected optimal design parameters with λ=0.10, H=8.64 gave better performance among the other designs, namely, average run length, ARL1=3.24 ~ 16.93 (for out-of-control process) and recognition accuracy, RA=89.05 ~ 97.73%. For in-control process, design parameters with λ=0.40, H=10.31 parameter gave superior performance with ARL0 = 676.81 ~ 921.71, which is more effective in avoiding false alarm with any correlation.
format	Thesis
qualification_name	Master of Philosophy (M.Phil.)
qualification_level	Master's degree
author	Mokhtar, Mohd Shukri
author_facet	Mokhtar, Mohd Shukri
author_sort	Mokhtar, Mohd Shukri
title	Design optimization for the two-stage bivariate pattern recognition scheme
title_short	Design optimization for the two-stage bivariate pattern recognition scheme
title_full	Design optimization for the two-stage bivariate pattern recognition scheme
title_fullStr	Design optimization for the two-stage bivariate pattern recognition scheme
title_full_unstemmed	Design optimization for the two-stage bivariate pattern recognition scheme
title_sort	design optimization for the two-stage bivariate pattern recognition scheme
granting_institution	Universiti Tun Hussein Onn Malaysia
granting_department	Faculty of Mechanical and Manufacturing Engineering
publishDate	2015
url	http://eprints.uthm.edu.my/1380/2/MOHD%20SHUKRI%20MOKHTAR%20COPYRIGHT%20DECLARATION.pdf http://eprints.uthm.edu.my/1380/1/24p%20MOHD%20SHUKRI%20MOKHTAR.pdf http://eprints.uthm.edu.my/1380/3/MOHD%20SHUKRI%20MOKHTAR%20WATERMARK.pdf
_version_	1747830777574326272

Design optimization for the two-stage bivariate pattern recognition scheme

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