University of Oulu

Knowledge-based decision support systems for production optimization and quality improvement in the electronics industry

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Author: Gebus, Sébastien1,2
Organizations: 1University of Oulu, Faculty of Technology, Department of Process and Environmental Engineering
2University of Oulu, Faculty of Technology, Control Engineering Laboratory
Format: ebook
Version: published version
Access: open
Online Access: PDF Full Text (PDF, 2.5 MB)
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Language: English
Published: 2006
Publish Date: 2006-09-12
Thesis type: Doctoral Dissertation
Defence Note: Academic dissertation to be presented, with the assent of the Faculty of Technology of the University of Oulu, for public defence in Kuusamonsali (Auditorium YB210), Linnanmaa, on September 22nd, 2006, at 12 noon
Reviewer: Professor Tapio Frantti
Professor Kari Koskinen


For the past few years, electronics manufacturing may have been the victim of its own success. Unlike in heavier industries, rationalization is a concept that was unknown in the sector until only a few years ago and even now, many companies are struggling with cost-cutting measures. Production systems in electronics manufacturing need to be highly flexible because of a varying and evolving environment. Therefore real-time process control and, possibly as a result, production optimization are extremely challenging areas. Traditional approaches often do not work due to a lack of robustness or reliability.

For this reason, a new generation of decision support systems is needed in response to some specific problems. The thesis addresses topics such as design of intelligent interfaces for knowledge acquisition and elicitation, use of that knowledge for improved data analysis and diagnostics, real-time feedback control, self-tuning capabilities, and evaluation of optimization methods in discrete processes. Topics covered therefore include the whole scope of a decision support system, from its design through to the evaluation of its performance as well as interaction capabilities as a vehicle for sharing information.

The aim of this research is to streamline the development of a new generation of decision support systems by providing tools and methods for a better integration of knowledge in an evolving environment. The main interest lies not only in improved data analysis, but also in better formalization and use of diagnosis. Case studies presented in this thesis demonstrate the practical feasibility of such an approach.

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Series: Acta Universitatis Ouluensis. C, Technica
ISSN-E: 1796-2226
ISBN: 951-42-8205-1
ISBN Print: 951-42-8204-3
Issue: 255
Copyright information: © University of Oulu, 2006. This publication is copyrighted. You may download, display and print it for your own personal use. Commercial use is prohibited.