ECAI 2004 Conference Paper

[full paper]

Control of a Hydroforming Press with Bayesian Networks

Rainer Deventer, Heinrich Niemann, Martino Celeghini

The demands to automatic control for industrial plants are growing due to an increased complexity of the manufacturing processes. To face these challenges, intelligent control is getting more and more important. For example, neural networks and Fuzzy Logic are regularly used. The usage of Bayesian networks is seldom mentioned even if many training algorithms are available and Bayesian networks are also able to act under real-time conditions. That means that main preconditions for a self adaptive controller are given. This paper explains how a Bayesian network is employed as a controller. The main idea is to use the desired value as if it were already observed and to use marginalization for the calculation of the input. This principle is successfully applied to the control of a hydroforming press. As a result the process characteristics in terms of an uniform blank draw-in and the preforming pressure are improved.

Keywords: Bayesian networks, Modelling of Manufacturing Processes, Control, Hydroforming

Citation: Rainer Deventer, Heinrich Niemann, Martino Celeghini: Control of a Hydroforming Press with Bayesian Networks. In R.López de Mántaras and L.Saitta (eds.): ECAI2004, Proceedings of the 16th European Conference on Artificial Intelligence, IOS Press, Amsterdam, 2004, pp.251-255.