: Westkämper, E.; Schmidt, T.

Abstract
Today's manufacturing methods are caught between the growing need for quality, high process safety, minimal manufacturing costs, and short manufacturing times. In order to meet these demands, process setting parameters have to be chosen in the best possible way, according to demand on quality. For such optimisation it is necessary to represent the number of influencing parameters, however, conventional approaches to modelling and optimisation are no longer sufficient. In this article it is shown how, by means of applying neural networks for process modelling, even these highly complex interdependencies can be learned. That way both process and quality parameters can be assessed before or during processing. By connecting them with corresponding cost models, it is possible to optimise processes with the help of evolutionary algorithms. Using examples of different manufacturing processes, the possibilities for process modelling and optimisation with neural networks and evolutionary algori thms are demonstrated.