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...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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mdpi
2023
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| Subjects: | |
| Online Access: | http://eprints.uthm.edu.my/8853/1/J15700_059e5b6d8fb9c3ee505a7faedffe6ac7.pdf |
| _version_ | 1796869847014965248 |
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| author | Waseem Alwan, Waseem Alwan Ngadiman, Nor Hasrul Akhmal Adnan Hassan, Adnan Hassan Syahril Ramadhan Saufi, Syahril Ramadhan Saufi Mahmood, Salwa |
| author_facet | Waseem Alwan, Waseem Alwan Ngadiman, Nor Hasrul Akhmal Adnan Hassan, Adnan Hassan Syahril Ramadhan Saufi, Syahril Ramadhan Saufi Mahmood, Salwa |
| author_sort | Waseem Alwan, Waseem Alwan |
| collection | UTHM |
| 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. |
| first_indexed | 2024-03-05T22:00:52Z |
| format | Article |
| id | uthm.eprints-8853 |
| institution | Universiti Tun Hussein Onn Malaysia |
| language | English |
| last_indexed | 2024-03-05T22:00:52Z |
| publishDate | 2023 |
| publisher | mdpi |
| record_format | dspace |
| spelling | uthm.eprints-88532023-06-18T01:31:35Z http://eprints.uthm.edu.my/8853/ Ensemble Classifier for Recognition of Small Variation in X-Bar Control Chart Patterns Waseem Alwan, Waseem Alwan Ngadiman, Nor Hasrul Akhmal Adnan Hassan, Adnan Hassan Syahril Ramadhan Saufi, Syahril Ramadhan Saufi 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/8853/1/J15700_059e5b6d8fb9c3ee505a7faedffe6ac7.pdf Waseem Alwan, Waseem Alwan and Ngadiman, Nor Hasrul Akhmal and Adnan Hassan, Adnan Hassan and Syahril Ramadhan Saufi, Syahril Ramadhan Saufi and Mahmood, Salwa (2023) Ensemble Classifier for Recognition of Small Variation in X-Bar Control Chart Patterns. Machines, 11 (1). pp. 1-33. https://doi.org/10.3390/machines11010115 |
| spellingShingle | T Technology (General) Waseem Alwan, Waseem Alwan Ngadiman, Nor Hasrul Akhmal Adnan Hassan, Adnan Hassan Syahril Ramadhan Saufi, Syahril Ramadhan Saufi Mahmood, Salwa Ensemble Classifier for Recognition of Small Variation in X-Bar Control Chart Patterns |
| title | 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_short | 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 |
| topic | T Technology (General) |
| url | http://eprints.uthm.edu.my/8853/1/J15700_059e5b6d8fb9c3ee505a7faedffe6ac7.pdf |
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