Statistical process control in stamping process using pattern recognition approach

Unnatural process variation (UPV) is a vital quality problem in the metal-stamping process that contributes to the causes of poor product quality. In the production site, sources of UPV are usually found based on special causes. Recently, there are still debates amongst researchers to find an effect...

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التفاصيل البيبلوغرافية
المؤلف الرئيسي:	Abdul Rahman, Norasulaini
التنسيق:	أطروحة
اللغة:	English English English
منشور في:	2018
الموضوعات:	TK7800-8360 Electronics
الوصول للمادة أونلاين:	http://eprints.uthm.edu.my/481/1/24p%20NORASULAINI%20ABDUL%20RAHMAN.pdf http://eprints.uthm.edu.my/481/2/NORASULAINI%20ABDUL%20RAHMAN%20COPYRIGHT%20DECLARATION.pdf http://eprints.uthm.edu.my/481/3/NORASULAINI%20ABDUL%20RAHMAN%20WATERMARK.pdf
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id	my-uthm-ep.481
record_format	uketd_dc
spelling	my-uthm-ep.4812021-07-25T07:04:01Z Statistical process control in stamping process using pattern recognition approach 2018-07 Abdul Rahman, Norasulaini TK7800-8360 Electronics Unnatural process variation (UPV) is a vital quality problem in the metal-stamping process that contributes to the causes of poor product quality. In the production site, sources of UPV are usually found based on special causes. Recently, there are still debates amongst researchers to find an effective technique for online monitoring and diagnosing UPV sources. Control chart pattern recognition (CCPR) is the most investigated technique. The existing CCPR framework was mainly developed by using raw data-based artificial neural network (ANN) recognisers, whereby the recognisers were trained by using artificially generated statistical process control (SPC) chart patterns. This is because the real SPC samples were commonly confidential, not economically available or difficult to obtain in a large amount for sufficient training. In this study, a new CCPR framework by using statistical features ANN recogniser with real SPC samples as the training control chart patterns was investigated. The SPC data were directly taken from the metal-stamping process. This framework is able to monitor and diagnose several root cause errors, such as dimension out, double punch, high burr and rivet slanting with a short recognition window size (10 subgroup samples). Based on dynamic data training, the proposed recogniser resulted in a better recognition accuracy (normal pattern = 100%, unnatural pattern = 100%) as compared to the raw data-based ANN (normal pattern = 66.67%, unnatural pattern = 26.92%). To ensure its applicability for real manufacturing process, the performance of the framework was successfully validated by using new SPC samples. This finding would be useful to avoid erroneous interpretation of root cause for defects amongst quality practitioners. 2018-07 Thesis http://eprints.uthm.edu.my/481/ http://eprints.uthm.edu.my/481/1/24p%20NORASULAINI%20ABDUL%20RAHMAN.pdf text en public http://eprints.uthm.edu.my/481/2/NORASULAINI%20ABDUL%20RAHMAN%20COPYRIGHT%20DECLARATION.pdf text en staffonly http://eprints.uthm.edu.my/481/3/NORASULAINI%20ABDUL%20RAHMAN%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	TK7800-8360 Electronics
spellingShingle	TK7800-8360 Electronics Abdul Rahman, Norasulaini Statistical process control in stamping process using pattern recognition approach
description	Unnatural process variation (UPV) is a vital quality problem in the metal-stamping process that contributes to the causes of poor product quality. In the production site, sources of UPV are usually found based on special causes. Recently, there are still debates amongst researchers to find an effective technique for online monitoring and diagnosing UPV sources. Control chart pattern recognition (CCPR) is the most investigated technique. The existing CCPR framework was mainly developed by using raw data-based artificial neural network (ANN) recognisers, whereby the recognisers were trained by using artificially generated statistical process control (SPC) chart patterns. This is because the real SPC samples were commonly confidential, not economically available or difficult to obtain in a large amount for sufficient training. In this study, a new CCPR framework by using statistical features ANN recogniser with real SPC samples as the training control chart patterns was investigated. The SPC data were directly taken from the metal-stamping process. This framework is able to monitor and diagnose several root cause errors, such as dimension out, double punch, high burr and rivet slanting with a short recognition window size (10 subgroup samples). Based on dynamic data training, the proposed recogniser resulted in a better recognition accuracy (normal pattern = 100%, unnatural pattern = 100%) as compared to the raw data-based ANN (normal pattern = 66.67%, unnatural pattern = 26.92%). To ensure its applicability for real manufacturing process, the performance of the framework was successfully validated by using new SPC samples. This finding would be useful to avoid erroneous interpretation of root cause for defects amongst quality practitioners.
format	Thesis
qualification_name	Master of Philosophy (M.Phil.)
qualification_level	Master's degree
author	Abdul Rahman, Norasulaini
author_facet	Abdul Rahman, Norasulaini
author_sort	Abdul Rahman, Norasulaini
title	Statistical process control in stamping process using pattern recognition approach
title_short	Statistical process control in stamping process using pattern recognition approach
title_full	Statistical process control in stamping process using pattern recognition approach
title_fullStr	Statistical process control in stamping process using pattern recognition approach
title_full_unstemmed	Statistical process control in stamping process using pattern recognition approach
title_sort	statistical process control in stamping process using pattern recognition approach
granting_institution	Universiti Tun Hussein Onn Malaysia
granting_department	Faculty of Mechanical and Manufacturing Engineering
publishDate	2018
url	http://eprints.uthm.edu.my/481/1/24p%20NORASULAINI%20ABDUL%20RAHMAN.pdf http://eprints.uthm.edu.my/481/2/NORASULAINI%20ABDUL%20RAHMAN%20COPYRIGHT%20DECLARATION.pdf http://eprints.uthm.edu.my/481/3/NORASULAINI%20ABDUL%20RAHMAN%20WATERMARK.pdf
_version_	1747830620691628032

Statistical process control in stamping process using pattern recognition approach

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