Abstract

Modern manufacturing systems have incorporated various “intelligent” decision support techniques and algorithms in the form of rules-based systems which have been promoted extensively because of the recent development in fuzzy inference, artificial neural network, machine learning and optimization algorithms, and etc. The basic elements of a modern manufacturing system include design automation (CAD and CAPP), production automation (CAM and MRP II), and shop floor automation (SFC and FMS), which must then be integrated into a coherent system through information automation (distributed DBMS and workflow automation) to form a fully automated factory. Therefore, considerable amount of rule-based systems have been developed and implemented fully or partially into each of the above areas of automation. To build a robust rule-based system requires a suitable modeling and analysis tool and a systematic design methodology. By incorporating neural network concept and associated learning algorithm, we present in this paper a Petri net based modeling methodology which can be used to design and implement adaptive rule-based systems for control and optimization in intelligent manufacturing systems. Demonstration is done on parametric design and optimization in manufacturing processes.

This content is only available via PDF.
You do not currently have access to this content.