Control chart pattern recognition using small window size for identifying bivariate process mean shifts
There are many traits in the manufacturing technology to assure the quality of products. One of the current practices aims for monitoring the in-process quality of small-lot production using Statistical Process Control (SPC), which requires small samples or small window sizes. In this study, the rec...
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Penerbit UTHM
2021
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| Online Access: | http://eprints.uthm.edu.my/3753/1/J12571_dbdbdc6b98c363f5fad729580c5fe8b7.pdf http://eprints.uthm.edu.my/3753/ https://doi.org/10.30880/ijie.2021.13.02.024 |
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| author | Kasmin, A. Masood, I. Abdul Rahman, N. Abdul Kadir, A. H. Abdol Rahman, M. N. |
| author_facet | Kasmin, A. Masood, I. Abdul Rahman, N. Abdul Kadir, A. H. Abdol Rahman, M. N. |
| author_sort | Kasmin, A. |
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| content_provider | Universiti Tun Hussein Onn Malaysia |
| content_source | UTHM Institutional Repository |
| continent | Asia |
| country | Malaysia |
| description | There are many traits in the manufacturing technology to assure the quality of products. One of the current practices aims for monitoring the in-process quality of small-lot production using Statistical Process Control (SPC), which requires small samples or small window sizes. In this study, the recognition performance of bivariate SPC pattern recognition scheme was investigated when dealing with small window sizes (less than 24). The framework of the scheme was constructed using an artificial neural network recognizer. The simulated SPC samples in different window sizes (8 ~ 24) and different change points (fixed and varies) were generated to study the recognition performance of the scheme based on mean square error (MSE) and classification accuracy (CA) measures. Two main findings have been suggested: (i) the scheme was superior when recognizing shift patterns with various change points compared to the shift patterns with fixed change point, with lower MSE and higher CA results, (ii) the scheme was more difficult to recognize smaller window size patterns with increasing MSE and decreasing CA trends, since these patterns provided insufficient information of unnatural variation. The outcome of this study would be helpful for industrial practitioners towards applying SPC for small-lot-production. |
| format | Article |
| id | my.uthm.eprints-3753 |
| institution | Universiti Tun Hussein Onn Malaysia |
| language | en |
| publishDate | 2021 |
| publisher | Penerbit UTHM |
| record_format | eprints |
| spelling | my.uthm.eprints-37532021-11-22T01:51:24Z http://eprints.uthm.edu.my/3753/ Control chart pattern recognition using small window size for identifying bivariate process mean shifts Kasmin, A. Masood, I. Abdul Rahman, N. Abdul Kadir, A. H. Abdol Rahman, M. N. TS155-194 Production management. Operations management There are many traits in the manufacturing technology to assure the quality of products. One of the current practices aims for monitoring the in-process quality of small-lot production using Statistical Process Control (SPC), which requires small samples or small window sizes. In this study, the recognition performance of bivariate SPC pattern recognition scheme was investigated when dealing with small window sizes (less than 24). The framework of the scheme was constructed using an artificial neural network recognizer. The simulated SPC samples in different window sizes (8 ~ 24) and different change points (fixed and varies) were generated to study the recognition performance of the scheme based on mean square error (MSE) and classification accuracy (CA) measures. Two main findings have been suggested: (i) the scheme was superior when recognizing shift patterns with various change points compared to the shift patterns with fixed change point, with lower MSE and higher CA results, (ii) the scheme was more difficult to recognize smaller window size patterns with increasing MSE and decreasing CA trends, since these patterns provided insufficient information of unnatural variation. The outcome of this study would be helpful for industrial practitioners towards applying SPC for small-lot-production. Penerbit UTHM 2021 Article PeerReviewed text en http://eprints.uthm.edu.my/3753/1/J12571_dbdbdc6b98c363f5fad729580c5fe8b7.pdf Kasmin, A. and Masood, I. and Abdul Rahman, N. and Abdul Kadir, A. H. and Abdol Rahman, M. N. (2021) Control chart pattern recognition using small window size for identifying bivariate process mean shifts. The International Journal of Integrated Engineering, 13 (2). pp. 208-213. ISSN 2229-838X https://doi.org/10.30880/ijie.2021.13.02.024 |
| spellingShingle | TS155-194 Production management. Operations management Kasmin, A. Masood, I. Abdul Rahman, N. Abdul Kadir, A. H. Abdol Rahman, M. N. Control chart pattern recognition using small window size for identifying bivariate process mean shifts |
| title | Control chart pattern recognition using small window size for identifying bivariate process mean shifts |
| title_full | Control chart pattern recognition using small window size for identifying bivariate process mean shifts |
| title_fullStr | Control chart pattern recognition using small window size for identifying bivariate process mean shifts |
| title_full_unstemmed | Control chart pattern recognition using small window size for identifying bivariate process mean shifts |
| title_short | Control chart pattern recognition using small window size for identifying bivariate process mean shifts |
| title_sort | control chart pattern recognition using small window size for identifying bivariate process mean shifts |
| topic | TS155-194 Production management. Operations management |
| url | http://eprints.uthm.edu.my/3753/1/J12571_dbdbdc6b98c363f5fad729580c5fe8b7.pdf http://eprints.uthm.edu.my/3753/ https://doi.org/10.30880/ijie.2021.13.02.024 |
| url_provider | http://eprints.uthm.edu.my/ |