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Multivariate quality control using an integrated artificial neural network scheme: a case study in plastic injection molding industry

Mohd Khairy, Afiz Azry (2013) Multivariate quality control using an integrated artificial neural network scheme: a case study in plastic injection molding industry. Masters thesis, Universiti Tun Hussein Onn Malaysia.

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Abstract

Manufacturers using traditional process control charts to monitor their processes often encounter out-of-control signals indicating that the process mean has changed. Most manufacturers are unaware how much these changes in the mean inflate the variance in the process output. In actual, many manufacturing processes involve two or more dependent variables and attempting to monitor such variables separately using univariate SPC charting scheme would increase false alarms and leading to wrong decision making The problem becomes more complicated when dealing with small mean shift particularly in identifying the causable variables. In this project study, advances SPC scheme which applied Artificial Neural Networks that were designed to enable balanced monitoring and accurate diagnosis were used. Its performance and effectiveness in actual manufacturing practice were compared with common use traditional process control chart. Thermoplastic Injection Molding was selected in this study since most of the industries applied this method to produce low cost parts. It is important to ensure that good quality parts can be produced according to requirement. The potential benefit from advance SPC schemes was it able to performed rapid detection of process disturbance. However the accuracy of mean shifted diagnosis performance need to improve.

Item Type: Thesis (Masters)
Subjects: T Technology > TS Manufactures > TS155-194 Production management. Operations management
Depositing User: Normajihan Abd. Rahman
Date Deposited: 06 Jan 2014 07:59
Last Modified: 06 Jan 2014 07:59
URI: http://eprints.uthm.edu.my/id/eprint/4680
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