Monitoring Variability of Multivariate-Attribute Processes Using Artificial Neural Network
Nowadays in some manufacturing processes, the quality of a product or process is well expressed by both correlated attribute and variable quality characteristics. To best of our knowledge, there is no method for monitoring the covariance matrix of multivariate-attribute quality characteristics. In t...
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| Format: | Article |
| Language: | fas |
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University of Isfahan
2015-01-01
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| Series: | مدیریت تولید و عملیات |
| Subjects: | |
| Online Access: | http://uijs.ui.ac.ir/jpom/browse.php?a_code=A-10-173-2&slc_lang=en&sid=1 |
| _version_ | 1797706084565123072 |
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| author | Amirhossein Amiri Mohammad Reza Maleki Mohammad Hadi Doroudyan |
| author_facet | Amirhossein Amiri Mohammad Reza Maleki Mohammad Hadi Doroudyan |
| author_sort | Amirhossein Amiri |
| collection | DOAJ |
| description | Nowadays in some manufacturing processes, the
quality of a product or process is well expressed by both correlated attribute
and variable quality characteristics. To best of our knowledge, there is no
method for monitoring the covariance matrix of multivariate-attribute
quality characteristics. In this paper, we propose a multi-layer perception
artificial neural network to monitor multivariate-attribute processes as well
as to diagnose the quality characteristic(s) responsible for out-of-control
signals. The performance of the proposed method is evaluated through a
numerical example from both detection and diagnosis perspectives. In addition,
the performance of the proposed neural network in detecting shifts in the
variance of quality characteristics is compared with two statistical methods
first proposed for monitoring the variability of multivariate quality
characteristics and developed in this paper for our problem. The results of numerical example show that the proposed
artificial neural network outperforms the extended statistical methods in
detecting different out-of-control shifts. The results also confirm that the
performance of the proposed neural network in identifying the quality
characteristic(s) responsible for out-of-control signal is satisfactory |
| first_indexed | 2024-03-12T05:45:59Z |
| format | Article |
| id | doaj.art-d66257dab4694fe1b9be0c8c864ffe44 |
| institution | Directory Open Access Journal |
| issn | 2251-6409 2423-6950 |
| language | fas |
| last_indexed | 2024-03-12T05:45:59Z |
| publishDate | 2015-01-01 |
| publisher | University of Isfahan |
| record_format | Article |
| series | مدیریت تولید و عملیات |
| spelling | doaj.art-d66257dab4694fe1b9be0c8c864ffe442023-09-03T05:38:12ZfasUniversity of Isfahanمدیریت تولید و عملیات2251-64092423-69502015-01-01523621Monitoring Variability of Multivariate-Attribute Processes Using Artificial Neural NetworkAmirhossein Amiri0Mohammad Reza Maleki1Mohammad Hadi Doroudyan2 Shahed University Shahed University Yazd University Nowadays in some manufacturing processes, the quality of a product or process is well expressed by both correlated attribute and variable quality characteristics. To best of our knowledge, there is no method for monitoring the covariance matrix of multivariate-attribute quality characteristics. In this paper, we propose a multi-layer perception artificial neural network to monitor multivariate-attribute processes as well as to diagnose the quality characteristic(s) responsible for out-of-control signals. The performance of the proposed method is evaluated through a numerical example from both detection and diagnosis perspectives. In addition, the performance of the proposed neural network in detecting shifts in the variance of quality characteristics is compared with two statistical methods first proposed for monitoring the variability of multivariate quality characteristics and developed in this paper for our problem. The results of numerical example show that the proposed artificial neural network outperforms the extended statistical methods in detecting different out-of-control shifts. The results also confirm that the performance of the proposed neural network in identifying the quality characteristic(s) responsible for out-of-control signal is satisfactoryhttp://uijs.ui.ac.ir/jpom/browse.php?a_code=A-10-173-2&slc_lang=en&sid=1Statistical process control Artificial neural network Multi-layer perceptron Multivariate-attribute process Average run length |
| spellingShingle | Amirhossein Amiri Mohammad Reza Maleki Mohammad Hadi Doroudyan Monitoring Variability of Multivariate-Attribute Processes Using Artificial Neural Network مدیریت تولید و عملیات Statistical process control Artificial neural network Multi-layer perceptron Multivariate-attribute process Average run length |
| title | Monitoring Variability of Multivariate-Attribute Processes Using Artificial Neural Network |
| title_full | Monitoring Variability of Multivariate-Attribute Processes Using Artificial Neural Network |
| title_fullStr | Monitoring Variability of Multivariate-Attribute Processes Using Artificial Neural Network |
| title_full_unstemmed | Monitoring Variability of Multivariate-Attribute Processes Using Artificial Neural Network |
| title_short | Monitoring Variability of Multivariate-Attribute Processes Using Artificial Neural Network |
| title_sort | monitoring variability of multivariate attribute processes using artificial neural network |
| topic | Statistical process control Artificial neural network Multi-layer perceptron Multivariate-attribute process Average run length |
| url | http://uijs.ui.ac.ir/jpom/browse.php?a_code=A-10-173-2&slc_lang=en&sid=1 |
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