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Bivariate quality control using two-stage intelligent monitoring scheme

In manufacturing industries, it is well known that process variation is a major source of poor quality products. As such, monitoring and diagnosis of variation is essential towards continuous quality improvement. This becomes more challenging when involving two correlated variables (bivariate), wher...

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التفاصيل البيبلوغرافية
المؤلفون الرئيسيون: Masood, Ibrahim, Hassan, Adnan
التنسيق: مقال
منشور في: Elsevier Ltd. 2014
الموضوعات:
TJ Mechanical engineering and machinery
الوصول للمادة أونلاين:http://eprints.utm.my/id/eprint/52021/
http://dx.doi.org/10.1016/j.eswa.2014.05.042
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id my.utm.52021
record_format eprints
spelling my.utm.520212018-11-30T07:00:21Z http://eprints.utm.my/id/eprint/52021/ Bivariate quality control using two-stage intelligent monitoring scheme Masood, Ibrahim Hassan, Adnan TJ Mechanical engineering and machinery In manufacturing industries, it is well known that process variation is a major source of poor quality products. As such, monitoring and diagnosis of variation is essential 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. Nevertheless, the existing traditional SPC schemes for bivariate quality control (BQC) were mainly designed for rapid detection of unnatural variation with limited capability in avoiding false alarm, that is, imbalanced monitoring performance. Another issue is the difficulty in identifying the source of unnatural variation, that is, lack of diagnosis, especially when dealing with small shifts. In this research, a scheme to address balanced monitoring and accurate diagnosis was investigated. Design consideration involved extensive simulation experiments to select input representation based on raw data and statistical features, artificial neural network recognizer design based on synergistic model, and monitoring-diagnosis approach based on two-stage technique. The study focused on bivariate process for cross correlation function, ρ = 0.1-0.9 and mean shifts, μ = ±0.75-3.00 standard deviations. The proposed two-stage intelligent monitoring scheme (2S-IMS) gave superior performance, namely, average run length, ARL1 = 3.18-16.75 (for out-of-control process), ARL0 = 335.01-543.93 (for in-control process) and recognition accuracy, RA = 89.5-98.5%. This scheme was validated in manufacturing of audio video device component. This research has provided a new perspective in realizing balanced monitoring and accurate diagnosis in BQC. Elsevier Ltd. 2014 Article PeerReviewed Masood, Ibrahim and Hassan, Adnan (2014) Bivariate quality control using two-stage intelligent monitoring scheme. Expert Systems with Applications, 41 (16). pp. 7579-7595. ISSN 0957-4174 http://dx.doi.org/10.1016/j.eswa.2014.05.042 DOI: 10.1016/j.eswa.2014.05.042
institution Universiti Teknologi Malaysia
building UTM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Teknologi Malaysia
content_source UTM Institutional Repository
url_provider http://eprints.utm.my/
topic TJ Mechanical engineering and machinery
spellingShingle TJ Mechanical engineering and machinery
Masood, Ibrahim
Hassan, Adnan
Bivariate quality control using two-stage intelligent monitoring scheme
description In manufacturing industries, it is well known that process variation is a major source of poor quality products. As such, monitoring and diagnosis of variation is essential 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. Nevertheless, the existing traditional SPC schemes for bivariate quality control (BQC) were mainly designed for rapid detection of unnatural variation with limited capability in avoiding false alarm, that is, imbalanced monitoring performance. Another issue is the difficulty in identifying the source of unnatural variation, that is, lack of diagnosis, especially when dealing with small shifts. In this research, a scheme to address balanced monitoring and accurate diagnosis was investigated. Design consideration involved extensive simulation experiments to select input representation based on raw data and statistical features, artificial neural network recognizer design based on synergistic model, and monitoring-diagnosis approach based on two-stage technique. The study focused on bivariate process for cross correlation function, ρ = 0.1-0.9 and mean shifts, μ = ±0.75-3.00 standard deviations. The proposed two-stage intelligent monitoring scheme (2S-IMS) gave superior performance, namely, average run length, ARL1 = 3.18-16.75 (for out-of-control process), ARL0 = 335.01-543.93 (for in-control process) and recognition accuracy, RA = 89.5-98.5%. This scheme was validated in manufacturing of audio video device component. This research has provided a new perspective in realizing balanced monitoring and accurate diagnosis in BQC.
format Article
author Masood, Ibrahim
Hassan, Adnan
author_facet Masood, Ibrahim
Hassan, Adnan
author_sort Masood, Ibrahim
title Bivariate quality control using two-stage intelligent monitoring scheme
title_short Bivariate quality control using two-stage intelligent monitoring scheme
title_full Bivariate quality control using two-stage intelligent monitoring scheme
title_fullStr Bivariate quality control using two-stage intelligent monitoring scheme
title_full_unstemmed Bivariate quality control using two-stage intelligent monitoring scheme
title_sort bivariate quality control using two-stage intelligent monitoring scheme
publisher Elsevier Ltd.
publishDate 2014
url http://eprints.utm.my/id/eprint/52021/
http://dx.doi.org/10.1016/j.eswa.2014.05.042
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score 13.251813

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