An application of spatial Iterative Learning Control to micro-additive manufacturing

Abstract

This manuscript details the first experimental application of a spatial Iterative Learning Control (SILC) update law recently described by the authors. Previous work introduced SILC, the SILC mathematical framework that was based on a 2D impulse response description of spatial dynamics, stability analyses, and a simulation study. Herein, we describe the physical system comprised of a micro-scale additive manufacturing system termed electrohydrodynamic jet printing and a spatial metrology system, atomic force microscopy. For this process, process variability is pronounced (± 15% from nominal) and thus complicates the use of heuristics-based process maps. This manuscript demonstrates that SILC applied to this combined manufacturing and in situ metrology system is capable, through successive iterations, of automatically creating a material droplet array that approximates an ideal material topography by compensating for process variability. These results give promise to more rigorous experimental trials involving multi-material and multi-layer micro additive manufacturing.