Small-Sample Learning of 3D Printed Thin-Wall Structures Using Printing Primitives

Yuanxiang Wang, Qiang Huang

Abstract: Despite its capability of fabricating highly complicated geometries, additive manufacturing (AM) or three-dimensional (3D) printing faces significant challenges in quality control, particularly the geometric accuracy of printed thin walls. Important to automotive, aerospace, and medical applications, this category of products tend to distort and deviate from their nominal designs. To establish a systematic accuracy modeling and control approach for 3D printed thin-wall structures, this study develops a small-sample learning approach using printing primitives. By treating each product as a combination of printing primitives, we overcome the small-data challenge by transforming a small set of training products into a large sample of geometric primitives with covariates of sizes and locations. To incorporate the process knowledge, we model the stack-up of primitives into thin walls through a convolution formulation of AM processes. A real case study shows the promise of the proposed small-sample learning method for accuracy control of 3D printed thin walls.