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Ensemble Classifier for Recognition of Small Variation in X-Bar Control Chart Patterns

Manufacturing processes have become highly accurate and precise in recent years, particularly in the chemical, aerospace, and electronics industries. This has attracted researchers to investigate improved procedures for monitoring and detection of small process variations to remain in line with such...

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Main Authors: Alwan, Waseem, Ngadiman, Nor Hasrul Akhmal, Hassan, Adnan, Saufi, Syahril Ramadhan, Mahmood, Salwa
Format: Article
Language:English
Published: Mdpi 2023
Subjects:
T Technology (General)
Online Access:http://eprints.uthm.edu.my/9021/1/J15700_059e5b6d8fb9c3ee505a7faedffe6ac7.pdf
http://eprints.uthm.edu.my/9021/
https://doi.org/10.3390/machines11010115
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spelling my.uthm.eprints.90212023-06-25T07:04:52Z http://eprints.uthm.edu.my/9021/ Ensemble Classifier for Recognition of Small Variation in X-Bar Control Chart Patterns Alwan, Waseem Ngadiman, Nor Hasrul Akhmal Hassan, Adnan Saufi, Syahril Ramadhan Mahmood, Salwa T Technology (General) Manufacturing processes have become highly accurate and precise in recent years, particularly in the chemical, aerospace, and electronics industries. This has attracted researchers to investigate improved procedures for monitoring and detection of small process variations to remain in line with such advances. Among these techniques, statistical process controls (SPC), in particular the control chart pattern (CCP), have become a popular choice for monitoring process variance, being utilized in numerous industrial and manufacturing applications. This study provides an improved control chart pattern recognition (CCPR) method focusing on X-bar chart patterns of small process variations using an ensemble classifier comprised of five complementing algorithms: decision tree, artificial neural network, linear support vector machine, Gaussian support vector machine, and k-nearest neighbours. Before advancing to the classification step, Nelson’s Rus Rules were utilized as a monitoring rule to distinguish between stable and unstable processes. The study’s findings indicate that the proposed method improves classification performance for patterns with mean changes of less than 1.5 sigma, and confirm that the performance of the ensemble classifier is superior to that of the individual classifier. The ensemble classifier can distinguish unstable pattern types with a classification accuracy of 99.55% and an ARL1 of 11.94. Mdpi 2023 Article PeerReviewed text en http://eprints.uthm.edu.my/9021/1/J15700_059e5b6d8fb9c3ee505a7faedffe6ac7.pdf Alwan, Waseem and Ngadiman, Nor Hasrul Akhmal and Hassan, Adnan and Saufi, Syahril Ramadhan and Mahmood, Salwa (2023) Ensemble Classifier for Recognition of Small Variation in X-Bar Control Chart Patterns. Machines. pp. 1-33. https://doi.org/10.3390/machines11010115
institution Universiti Tun Hussein Onn Malaysia
building UTHM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Tun Hussein Onn Malaysia
content_source UTHM Institutional Repository
url_provider http://eprints.uthm.edu.my/
language English
topic T Technology (General)
spellingShingle T Technology (General)
Alwan, Waseem
Ngadiman, Nor Hasrul Akhmal
Hassan, Adnan
Saufi, Syahril Ramadhan
Mahmood, Salwa
Ensemble Classifier for Recognition of Small Variation in X-Bar Control Chart Patterns
description Manufacturing processes have become highly accurate and precise in recent years, particularly in the chemical, aerospace, and electronics industries. This has attracted researchers to investigate improved procedures for monitoring and detection of small process variations to remain in line with such advances. Among these techniques, statistical process controls (SPC), in particular the control chart pattern (CCP), have become a popular choice for monitoring process variance, being utilized in numerous industrial and manufacturing applications. This study provides an improved control chart pattern recognition (CCPR) method focusing on X-bar chart patterns of small process variations using an ensemble classifier comprised of five complementing algorithms: decision tree, artificial neural network, linear support vector machine, Gaussian support vector machine, and k-nearest neighbours. Before advancing to the classification step, Nelson’s Rus Rules were utilized as a monitoring rule to distinguish between stable and unstable processes. The study’s findings indicate that the proposed method improves classification performance for patterns with mean changes of less than 1.5 sigma, and confirm that the performance of the ensemble classifier is superior to that of the individual classifier. The ensemble classifier can distinguish unstable pattern types with a classification accuracy of 99.55% and an ARL1 of 11.94.
format Article
author Alwan, Waseem
Ngadiman, Nor Hasrul Akhmal
Hassan, Adnan
Saufi, Syahril Ramadhan
Mahmood, Salwa
author_facet Alwan, Waseem
Ngadiman, Nor Hasrul Akhmal
Hassan, Adnan
Saufi, Syahril Ramadhan
Mahmood, Salwa
author_sort Alwan, Waseem
title Ensemble Classifier for Recognition of Small Variation in X-Bar Control Chart Patterns
title_short Ensemble Classifier for Recognition of Small Variation in X-Bar Control Chart Patterns
title_full Ensemble Classifier for Recognition of Small Variation in X-Bar Control Chart Patterns
title_fullStr Ensemble Classifier for Recognition of Small Variation in X-Bar Control Chart Patterns
title_full_unstemmed Ensemble Classifier for Recognition of Small Variation in X-Bar Control Chart Patterns
title_sort ensemble classifier for recognition of small variation in x-bar control chart patterns
publisher Mdpi
publishDate 2023
url http://eprints.uthm.edu.my/9021/1/J15700_059e5b6d8fb9c3ee505a7faedffe6ac7.pdf
http://eprints.uthm.edu.my/9021/
https://doi.org/10.3390/machines11010115
_version_ 1769845128038449152
score 13.211869

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