Modeling inter-layer interactions for out-of-plane shape deviation reduction in additive manufacturing

Shape accuracy is an important quality measure of finished parts built by Additive Manufacturing (AM) processes. Previous work has established a generic and prescriptive methodology to represent, predict and compensate in-plane (x – y plane) shape deviation of AM built products using a limited number of test cases. However, extension to the out-of-plane (z-plane) shape deviation faces a major challenge due to intricate inter-layer interactions and error accumulation. One direct manifestation of such complication is that products of the same shape exhibit different deviation patterns when varying product sizes. 
This work devises an economic experimental plan and a data analytical approach to model out-of-plane deviation for improving the understanding of inter-layer interactions using a small set of training shapes. The key strategy is to discover the transition of deviation patterns from a smaller shape with fewer layers to a bigger one with more layers. This transition is established through the effect equivalence principle, which enables the model predicting a smaller shape to digitally “reproduce” the bigger shape by identifying the equivalent amount of design adjustment. In addition, a Bayesian approach is established to infer the deviation models capable of predicting deviation of complex shapes along the z-direction. Furthermore, prediction and compensation of out-of-plane deviation for two-dimensional freeform shapes are accomplished with experimental validation in a stereolithography process.