MONITORING VARIABILITY OF MULTIVARIATE-ATTRIBUTE PROCESSES USING ARTIFICIAL NEURAL NETWORK

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AMIRI AMIR HOSSEIN; MALEKI MOHAMMAD REZA; DOROUDYAN MOHAMMAD HADI

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Paper Information

Journal: PRODUCTION AND OPERATIONS MANAGEMENT Year:2014 | Volume:5 | Issue:2 (9) Page(s): 21-36

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Information Journal Paper

Title

MONITORING VARIABILITY OF MULTIVARIATE-ATTRIBUTE PROCESSES USING ARTIFICIAL NEURAL NETWORK

Author(s)

AMIRI AMIR HOSSEIN | MALEKI MOHAMMAD REZA | DOROUDYAN MOHAMMAD HADI | Issue Writer Certificate

Keywords

STATISTICAL PROCESS CONTROLQ2

ARTIFICIAL NEURAL NETWORKQ2

MULTI-LAYER PERCEPTRONQ2

MULTIVARIATE-ATTRIBUTE PROCESSQ3

AVERAGE RUN LENGTHQ2

Abstract

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.

Cites

STEP CHANGE POINT ESTIMATION IN THE MULTIVARIATE-ATTRIBUTE PROCESS VARIABILITY USING ARTIFICIAL NEURAL NETWORKS AND MAXIMUM LIKELIHOOD ESTIMATION

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APA: Copy

AMIRI, AMIR HOSSEIN, MALEKI, MOHAMMAD REZA, & DOROUDYAN, MOHAMMAD HADI. (2014). MONITORING VARIABILITY OF MULTIVARIATE-ATTRIBUTE PROCESSES USING ARTIFICIAL NEURAL NETWORK. PRODUCTION AND OPERATIONS MANAGEMENT, 5(2 (9)), 21-36. SID. https://sid.ir/paper/217577/en

Vancouver: Copy

AMIRI AMIR HOSSEIN, MALEKI MOHAMMAD REZA, DOROUDYAN MOHAMMAD HADI. MONITORING VARIABILITY OF MULTIVARIATE-ATTRIBUTE PROCESSES USING ARTIFICIAL NEURAL NETWORK. PRODUCTION AND OPERATIONS MANAGEMENT[Internet]. 2014;5(2 (9)):21-36. Available from: https://sid.ir/paper/217577/en

IEEE: Copy

AMIR HOSSEIN AMIRI, MOHAMMAD REZA MALEKI, and MOHAMMAD HADI DOROUDYAN, “MONITORING VARIABILITY OF MULTIVARIATE-ATTRIBUTE PROCESSES USING ARTIFICIAL NEURAL NETWORK,” PRODUCTION AND OPERATIONS MANAGEMENT, vol. 5, no. 2 (9), pp. 21–36, 2014, [Online]. Available: https://sid.ir/paper/217577/en

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