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Flexible Automation and Integrated Manufacturing 1994
ISBN Print: 978-1-56700-018-4
KNOWLEDGE-BASED SYSTEMS FOR QUALITY CONTROL AND MONITORING
DOI: 10.1615/FAIM1994.590
pages 578-587
Abstract
International competitiveness requires high quality in combination with reduced innovation and throughput time. To improve product quality, quality control loops have to be build. In quality assurance three control loops are to be distinguished: within the machine, close to the machine and machines overlapping control loop. The objective of total quality management must be the integration of these horizontal control loops with vertical exchange of information. Originated by the given situation it becomes clear that high product quality can only be realised by highly developed and controlled processes. For future-oriented quality assurance it is necessary to evaluate process information in short sequences. To react quickly on discovered defects, quality information and data as well as expert knowledge must be available to the whole company. Commercial information systems are often not able to handle this information in a suitable way. Knowledge based systems, especially diagnostic systems, offer a solution for this problem. Online diagnostic systems can be used to build quality control loops close to the machine and machine overlapping.
This paper describes how to build quality control loops using online diagnostic systems. First the paper gives an insight of the use of knowledge based systems for quality control and monitoring. The differences between integrated control and diagnosis of process parameters are worked out. Finally the implementation of an online diagnostic system using knowledge based methods is introduced.
This paper describes how to build quality control loops using online diagnostic systems. First the paper gives an insight of the use of knowledge based systems for quality control and monitoring. The differences between integrated control and diagnosis of process parameters are worked out. Finally the implementation of an online diagnostic system using knowledge based methods is introduced.
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