Modern printing machines are designed to produce big quantities with high product quality in a short time. Due to large dead times in the control system, the first sheets have an insufficient quality and need to be wasted. In the context of decreasing lot sizes the total production costs increase. To improve the resource and cost efficiency, a model based control system was developed. The model parameter will be adapted by a neural network according to all relevant influence parameters, which enables high simulation accuracy. A data base contains all relevant production parameters and influences, which are used to train the neural network. It is designed to learn the correlation between 26 different influence parameters and the resulting system behaviour without any manual assistance. This paper describes a method to handle irregular distributed data sets to improve the training performance by using cluster algorithms. The initialization of the net weights is done according to the Nguyen-Widrow algorithm and different training algorithms are compared. Additionally, different network topologies are tested automatically to identify the best suited network. To combine the real time simulation model with the non-deterministic training process, the system is divided into two platforms. With the described control system it is possible to reduce the waste up to 80 % at the start of the production.
Schmid, M., Berger, S., & Reinhart, G. (2015). Cognitive parameter adaption for model based control systems. In Procedia CIRP (Vol. 33, pp. 133–138). Elsevier. https://doi.org/10.1016/j.procir.2015.06.025