Introducing Artificial Neural Networks to predict the dimensional and micro-geometrical deviations of additively manufactured parts

The dependencies between process parameters and the resulting geometrical accuracy of additively manufactured parts are usually highly nonlinear and thus complex to investigate and mathematically quantify. Therefore, the application of artificial intelligence techniques is promising to generate mathematical models that reduce effort and increase the prediction quality. The overall goal is to establish a procedure to automatically determine the optimal settings of the manufacturing process parameters to guarantee the highest geometrical accuracy of parts in additive manufactured production. This paper presents the first step towards this fully automatic procedure - the training and evaluation of a mathematical model based on artificial neural networks to quantify the effects of varying process parameters of a material extrusion process on both macro- and micro-geometrical performances. Therefore, a dataset is established based on the Design of Experiment of an additively manufactured part made from Polylactic Acid filament. The dataset is then used to train an artificial neural network that predicts the dimensional and micro-geometrical deviations of the manufactured parts. Finally, the evaluation of the network's prediction quality and reliability indicate that it is possible to predict the parameters linked to resulting print quality with a mean absolute error from 0.0004 to 0.036.