The error compensation for printed objects in
additive manufacturing (AM) has always been one of the most
critical problems. The precision control of the AM is usually
more difficult than the subtractive manufacturing system,
whose precision can reach the micron level easily by using a
servo system. For the AM, there usually exist shrinkage and
curling effects which lead to deformation. In this paper, we
focus on the in-plane shape deformation problem, and we build
the PredNet and CompNet, using deep neural networks for the
error prediction and compensation. We test our methods on
dental crown models. We generate deformed models by simulation
of the translation, scaling down and rotation deformation.
The minimum F1 scores of error prediction and compensation
can be up to 0.982.