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Diagnosis of bivariate process variation using an integrated MSPC-ANN scheme

Masood, Ibrahim and Ali, Rasheed Majeed and Mohd Solihin, Nurul Adlihisam and Elewe, Adel Muhsin (2016) Diagnosis of bivariate process variation using an integrated MSPC-ANN scheme. ARPN Journal of Engineering and Applied Sciences, 11 (12). pp. 7806-7812. ISSN 18196608

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Monitoring and diagnosis of mean shifts in manufacturing processes become more challenging when involving two or more correlated variables. Unfortunately, most of the existing multivariate statistical process control schemes are only effective in rapid detection but suffer high false alarm. This is referred to as imbalanced performance monitoring. The problem becomes more complicated when dealing with small mean shift particularly in identifying the causable variables. In this research, a scheme that integrated the control charting and pattern recognition technique has been investigated toward improving the quality control (QC) performance. Design considerations involved extensive simulation experiments to select input representation based on raw data and statistical features, recognizer design structure based on individual and Statistical Features-ANN models, and monitoring-diagnosis approach based on single stage and two stages techniques. The study focuses on correlated process mean shifts for cross correlation function, ρ = 0.1, 0.5, 0.9, and mean shift, μ = ± 0.75 ~ 3.00 standard deviations. Among the investigated design, an Integrated Multivariate Exponentially Weighted Moving Average with Artificial Neural Network scheme provides superior performance, namely the Average Run Length for grand average ARL1 = 7.55 ̴ 7.78 ( for out-of-control) and ARL0 = 4λ1.03 (small shifts) and 524.80 (large shifts) in control process and the grand average for recognition accuracy (RA) = λ6.36 ̴ λ8.74. This research has provided a new perspective in realizing balanced monitoring and accurate diagnosis of correlated process mean shifts.

Item Type: Article
Uncontrolled Keywords: Bivariate process; statistical process control; artificial neural network
Subjects: T Technology > TJ Mechanical engineering and machinery > TJ1-162 Mechanical engineering and machinery
Divisions: Faculty of Mechanical and Manufacturing Engineering > Department of Manufacturing and Industrial Engineering
Depositing User: Mr. Mohammad Shaifulrip Ithnin
Date Deposited: 13 Aug 2018 03:31
Last Modified: 13 Aug 2018 03:31
URI: http://eprints.uthm.edu.my/id/eprint/10029
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