IMC Strategy Using Neural Networks for 3D Printer Bed Temperature Control

Abstract

In this contribution, the temperature control of the 3D printer heatbed is observed. As the heat exchange power is strictly limited and the thermal process time constants are naturally around tens and hundreds of seconds, these processes are basically slow. The measuring of new data is time consuming, which can cause the profit loss in case of experiments in the production. Moreover, the finding of better control method can lead to significant monetary savings. One of the scopes of this article is to find out if it’s possible to built-up the neural network-based controller system together with the internal model control strategy providing better performance with data obtained in the production, where simply tuned PSD controller was used. The suitable order of the heating system is observed together with the size of the sampling period and neural network topology. The controllability with best performing neural networks is verified on the 3D printer heating bed.