Additive Manufacturing is a widely used technology; however, it also has several open
questions. In the modelling phase, it is necessary to predict undesired distortions.
There are several finite-element based simulation tools for this purpose, but these
are costly and resource-intensive. This paper presents a novel approach based on several
Machine Learning methods (decision trees, random forest, gradient boosted trees, support
vector machines, deep learning) to speed-up this process. The results show that it
is possible to give accurate predictions with these methods.
