Synergistic artificial neural network scheme for monitoring and diagnosis of multivariate process variation in mean shifts
In quality control, monitoring and diagnosis of multivariate out of control condition is essential in today’s manufacturing industries. The simplest case involves two correlated variables; for instance, monitoring value of temperature and pressure in our environment. Monitoring refers to the ide...
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| Format: | Thesis |
| Language: | English English |
| Published: |
2014
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| Online Access: | http://eprints.uthm.edu.my/1540/1/24p%20MOHD%20FAIRUZ%20MARIAN.pdf http://eprints.uthm.edu.my/1540/2/MOHD%20FAIRUZ%20MARIAN%20WATERMARK.pdf |
| _version_ | 1825709554624626688 |
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| author | Marian, Mohd Fairuz |
| author_facet | Marian, Mohd Fairuz |
| author_sort | Marian, Mohd Fairuz |
| collection | UTHM |
| description | In quality control, monitoring and diagnosis of multivariate out of control condition
is essential in today’s manufacturing industries. The simplest case involves two
correlated variables; for instance, monitoring value of temperature and pressure in
our environment. Monitoring refers to the identification of process condition either it
is running in control or out of control. Diagnosis refers to the identification of source
variables (X1 and X2) for out of control. In this study, a synergistic artificial neural
network scheme was investigated in quality control of process in plastic injection
moulding part. This process was selected since it less reported in the literature. In the
related point of view, this study should be useful in minimizing the cost of waste
materials. The result of this study, suggested this scheme has a superior performance
compared to the traditional control chart, namely Multivariate Exponentially
Weighted Moving Average (MEWMA). 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 variables. Whereby, diagnosis cannot be performed by
traditional control chart. This study is useful for quality control practitioner,
particularly in plastic injection moulding industry. |
| first_indexed | 2024-03-05T21:40:23Z |
| format | Thesis |
| id | uthm.eprints-1540 |
| institution | Universiti Tun Hussein Onn Malaysia |
| language | English English |
| last_indexed | 2024-03-05T21:40:23Z |
| publishDate | 2014 |
| record_format | dspace |
| spelling | uthm.eprints-15402021-10-03T07:57:51Z http://eprints.uthm.edu.my/1540/ Synergistic artificial neural network scheme for monitoring and diagnosis of multivariate process variation in mean shifts Marian, Mohd Fairuz TS Manufactures TS155-194 Production management. Operations management In quality control, monitoring and diagnosis of multivariate out of control condition is essential in today’s manufacturing industries. The simplest case involves two correlated variables; for instance, monitoring value of temperature and pressure in our environment. Monitoring refers to the identification of process condition either it is running in control or out of control. Diagnosis refers to the identification of source variables (X1 and X2) for out of control. In this study, a synergistic artificial neural network scheme was investigated in quality control of process in plastic injection moulding part. This process was selected since it less reported in the literature. In the related point of view, this study should be useful in minimizing the cost of waste materials. The result of this study, suggested this scheme has a superior performance compared to the traditional control chart, namely Multivariate Exponentially Weighted Moving Average (MEWMA). 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 variables. Whereby, diagnosis cannot be performed by traditional control chart. This study is useful for quality control practitioner, particularly in plastic injection moulding industry. 2014-07 Thesis NonPeerReviewed text en http://eprints.uthm.edu.my/1540/1/24p%20MOHD%20FAIRUZ%20MARIAN.pdf text en http://eprints.uthm.edu.my/1540/2/MOHD%20FAIRUZ%20MARIAN%20WATERMARK.pdf Marian, Mohd Fairuz (2014) Synergistic artificial neural network scheme for monitoring and diagnosis of multivariate process variation in mean shifts. Masters thesis, Universiti Tun Hussein Malaysia. |
| spellingShingle | TS Manufactures TS155-194 Production management. Operations management Marian, Mohd Fairuz Synergistic artificial neural network scheme for monitoring and diagnosis of multivariate process variation in mean shifts |
| title | Synergistic artificial neural network scheme for monitoring and diagnosis of multivariate process variation in mean shifts |
| title_full | Synergistic artificial neural network scheme for monitoring and diagnosis of multivariate process variation in mean shifts |
| title_fullStr | Synergistic artificial neural network scheme for monitoring and diagnosis of multivariate process variation in mean shifts |
| title_full_unstemmed | Synergistic artificial neural network scheme for monitoring and diagnosis of multivariate process variation in mean shifts |
| title_short | Synergistic artificial neural network scheme for monitoring and diagnosis of multivariate process variation in mean shifts |
| title_sort | synergistic artificial neural network scheme for monitoring and diagnosis of multivariate process variation in mean shifts |
| topic | TS Manufactures TS155-194 Production management. Operations management |
| url | http://eprints.uthm.edu.my/1540/1/24p%20MOHD%20FAIRUZ%20MARIAN.pdf http://eprints.uthm.edu.my/1540/2/MOHD%20FAIRUZ%20MARIAN%20WATERMARK.pdf |
| work_keys_str_mv | AT marianmohdfairuz synergisticartificialneuralnetworkschemeformonitoringanddiagnosisofmultivariateprocessvariationinmeanshifts |