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Design methodology of modular-ANN pattern recognizer for bivariate quality control

Mohd Sohaimi, Nurul Adlihisam and Masood, Ibrahim and Mohammad, Musli and Hassan, Mohd Fahrul (2017) Design methodology of modular-ANN pattern recognizer for bivariate quality control. Journal of Telecommunication, Electronic and Computer Engineering, 9 (3-2). pp. 31-34. ISSN 22898131

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Abstract

In quality control, monitoring unnatural variation (UV) in manufacturing process has become more challenging when dealing with two correlated variables (bivariate). The traditional multivariate statistical process control (MSPC) charts are only effective for triggering UV but unable to provide information towards diagnosis. In recent years, a branch of research has been focused on control chart pattern recognition (CCPR) technique. However, findings on the source of UV are still limited to sudden shifts patterns. In this study, a methodology to develop a CCPR scheme was proposed to identify various sources of UV based on shifts, trends, and cyclic patterns. The success factor for the scheme was outlined as a guideline for realizing accurate monitoring-diagnosis in bivariate quality control.

Item Type: Article
Uncontrolled Keywords: Bivariate quality control; control chart pattern recognition; modular neural network; unnatural variation
Subjects: Q Science > QA Mathematics > QA76 Computer software
Divisions: Faculty of Mechanical and Manufacturing Engineering > Department of Manufacturing and Industrial Engineering
Depositing User: Mr. Mohammad Shaifulrip Ithnin
Date Deposited: 20 Feb 2019 08:12
Last Modified: 20 Feb 2019 08:12
URI: http://eprints.uthm.edu.my/id/eprint/10732
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