Pattern recognition for manufacturing process variation using integrated statistical process control – artificial neural network
Variation in manufacturing process is known to be a major source of poor quality products and variation control is essential in quality improvement. In bivariate cases, which involve two correlated quality variables, the traditional statistical process control (SPC) charts are known to be effective...
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| Format: | Thesis |
| Language: | English English English |
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2015
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| Online Access: | http://eprints.uthm.edu.my/1279/2/AHMAD%20AZRIZAL%20MOHD%20ARIFFIN%20COPYRIGHT%20DECLARATION.pdf http://eprints.uthm.edu.my/1279/1/24p%20AHMAD%20AZRIZAL%20MOHD%20ARIFFIN.pdf http://eprints.uthm.edu.my/1279/3/AHMAD%20AZRIZAL%20MOHD%20ARIFFIN%20WATERMARK.pdf |
| _version_ | 1796868432890691584 |
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| author | Mohd Ariffin, Ahmad Azrizal |
| author_facet | Mohd Ariffin, Ahmad Azrizal |
| author_sort | Mohd Ariffin, Ahmad Azrizal |
| collection | UTHM |
| description | Variation in manufacturing process is known to be a major source of poor quality products and variation control is essential in quality improvement. In bivariate cases, which involve two correlated quality variables, the traditional statistical process control (SPC) charts are known to be effective in monitoring but they are lack of diagnosis. As such, process monitoring and diagnosis is critical 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. In this research, a scheme to address balanced monitoring and accurate diagnosis was investigated. Investigation has been focused on an integrated SPC - ANN model. This model utilizes the Exponentially Weighted Moving Average (EWMA) control chart and ANN model in two-stage monitoring and diagnosis technique. This scheme was validated in manufacturing of hard disc drive. The study focused on bivariate process for cross correlation function, ρ = 0.3 and 0.7 and mean shifts, μ = ±1.00-2.00 standard deviations. The result of this study, suggested this scheme has a superior performance compared to the traditional control chart. In monitoring, it is effective in rapid detection of out of control without false alarm. In diagnosis, it is able to accurately identify for source of variation. This scheme is effective for cases variations of such loading error, offsetting tool and inconsistent pressure. Therefore, this study should be useful in minimizing the cost of waste materials and has provided a new perspective in realizing balanced monitoring and accurate diagnosis in BQC. |
| first_indexed | 2024-03-05T21:39:41Z |
| format | Thesis |
| id | uthm.eprints-1279 |
| institution | Universiti Tun Hussein Onn Malaysia |
| language | English English English |
| last_indexed | 2024-03-05T21:39:41Z |
| publishDate | 2015 |
| record_format | dspace |
| spelling | uthm.eprints-12792021-09-30T07:02:28Z http://eprints.uthm.edu.my/1279/ Pattern recognition for manufacturing process variation using integrated statistical process control – artificial neural network Mohd Ariffin, Ahmad Azrizal TS155-194 Production management. Operations management Variation in manufacturing process is known to be a major source of poor quality products and variation control is essential in quality improvement. In bivariate cases, which involve two correlated quality variables, the traditional statistical process control (SPC) charts are known to be effective in monitoring but they are lack of diagnosis. As such, process monitoring and diagnosis is critical 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. In this research, a scheme to address balanced monitoring and accurate diagnosis was investigated. Investigation has been focused on an integrated SPC - ANN model. This model utilizes the Exponentially Weighted Moving Average (EWMA) control chart and ANN model in two-stage monitoring and diagnosis technique. This scheme was validated in manufacturing of hard disc drive. The study focused on bivariate process for cross correlation function, ρ = 0.3 and 0.7 and mean shifts, μ = ±1.00-2.00 standard deviations. The result of this study, suggested this scheme has a superior performance compared to the traditional control chart. In monitoring, it is effective in rapid detection of out of control without false alarm. In diagnosis, it is able to accurately identify for source of variation. This scheme is effective for cases variations of such loading error, offsetting tool and inconsistent pressure. Therefore, this study should be useful in minimizing the cost of waste materials and has provided a new perspective in realizing balanced monitoring and accurate diagnosis in BQC. 2015-07 Thesis NonPeerReviewed text en http://eprints.uthm.edu.my/1279/2/AHMAD%20AZRIZAL%20MOHD%20ARIFFIN%20COPYRIGHT%20DECLARATION.pdf text en http://eprints.uthm.edu.my/1279/1/24p%20AHMAD%20AZRIZAL%20MOHD%20ARIFFIN.pdf text en http://eprints.uthm.edu.my/1279/3/AHMAD%20AZRIZAL%20MOHD%20ARIFFIN%20WATERMARK.pdf Mohd Ariffin, Ahmad Azrizal (2015) Pattern recognition for manufacturing process variation using integrated statistical process control – artificial neural network. Masters thesis, Universiti Tun Hussein Onn Malaysia. |
| spellingShingle | TS155-194 Production management. Operations management Mohd Ariffin, Ahmad Azrizal Pattern recognition for manufacturing process variation using integrated statistical process control – artificial neural network |
| title | Pattern recognition for manufacturing process variation using integrated statistical process control – artificial neural network |
| title_full | Pattern recognition for manufacturing process variation using integrated statistical process control – artificial neural network |
| title_fullStr | Pattern recognition for manufacturing process variation using integrated statistical process control – artificial neural network |
| title_full_unstemmed | Pattern recognition for manufacturing process variation using integrated statistical process control – artificial neural network |
| title_short | Pattern recognition for manufacturing process variation using integrated statistical process control – artificial neural network |
| title_sort | pattern recognition for manufacturing process variation using integrated statistical process control artificial neural network |
| topic | TS155-194 Production management. Operations management |
| url | http://eprints.uthm.edu.my/1279/2/AHMAD%20AZRIZAL%20MOHD%20ARIFFIN%20COPYRIGHT%20DECLARATION.pdf http://eprints.uthm.edu.my/1279/1/24p%20AHMAD%20AZRIZAL%20MOHD%20ARIFFIN.pdf http://eprints.uthm.edu.my/1279/3/AHMAD%20AZRIZAL%20MOHD%20ARIFFIN%20WATERMARK.pdf |
| work_keys_str_mv | AT mohdariffinahmadazrizal patternrecognitionformanufacturingprocessvariationusingintegratedstatisticalprocesscontrolartificialneuralnetwork |