Abstract:

Due to the nature of low-volume fabrication of infinite product variety in additive manufacturing (AM), Machine Learning for AM (ML4AM) faces ``small data, big tasks" challenges to learn heterogeneous point cloud data and control the quality of new designs.  This work establishes an impulse response formulation of layer-wise AM processes to relate design inputs with the deformed final products. To enable prescriptive learning from a small sample of printed parts with different 3D shapes, we develop a fabrication-aware input-output representation, where each product is constructed by a large amount of basic shape primitives. The impulse response model depicts how the 2D shape in each layer are stacked up to become final 3D shape primitives. A geometric quality of a new design can therefore be predicted through the construction of learned shape primitives. Essentially, the small-sample learning of printed products is transformed into a large-sample learning of printed shape primitives under the impulse response formulation of AM. This fabrication-aware formulation builds the foundation for applying well-established control theory to the intelligent quality control in AM. It not only provides theoretical underpinning and justification of our previous work, but also enable new opportunities in ML4AM.