Surface Quality Characterization, Learning, and Prediction for 3D Printed 2D Free-Form Products Through Dimensional Reduction

Surface quality characterization is essential for ensuring product functionality as well as for guiding the design and the manufacturing process. Although surface quality characterization has been well studied in the context of mass production, it faces a number of challenges in additive manufacturing (AM). The infinite dimensionality of free-form geometric designs combined with a limited sample size of available products hinders the statistical analysis of product quality in AM. In addition, surface quality patterns in AM are driven by multiple geometric and process covariates such as size of the shape and location on the printing bed. As a result, conventional data-intensive characterization and modeling methods designed for mass production are ill-suited for AM. To address these challenges, the idea of printing primitives is utilized to reduce infinite two-dimensional (2D) free-form shapes into a finite set of simple primitives. A Gaussian process (GP) changepoint detection algorithm is applied to partition the 2D free-form shapes into basic segments with distinct surface quality characteristics. Then, we construct a surface quality characterization procedure using a GP representation of the surface quality in each class of shape primitives. Within each primitive, surface quality patterns are modeled using tensor product bases to efficiently capture the covariate dependency. The effectiveness of the proposed method is demonstrated in two actually printed AM sample sets.