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Abstract Iterative learning control (ILC) is a powerful technique to regulate repetitive systems. Additive manufacturing falls into this category by nature of its repetitive action in building three-dimensional structures in a layer-by-layer manner. In literature, spatial ILC (SILC) has been used in conjunction with additive processes to regulate single-layer structures with only one class of material. However, SILC has the unexplored potential to regulate additive manufacturing structures with multiple build materials in a three-dimensional fashion. Estimating the appropriate feedforward signal in these structures can be challenging due to iteration varying initial conditions, system parameters, and surface interaction dynamics in different layers of multi-material structures. In this paper, SILC is used as a recursive control strategy to iteratively construct the feedforward signal to improve part quality of 3D structures that consist of at least two materials in a layer-by-layer manner. The system dynamics are approximated by discrete 2D spatial convolution using kernels that incorporate in-layer and layer-to-layer variations. We leverage the existing SILC models in literature and extend them to account for the iteration varying uncertainties in the plant model to capture a more reliable representation of the multi-material additive process. The feasibility of the proposed diagonal framework was demonstrated using simulation results of an electrohydrodynamic jet printing (e-jet) printing process. 

Electrohydrodynamic jet (e‐jet) printing is a high‐resolution additive manufacturing technique that holds promise for the fabrication of customized micro‐devices. In this companion paper set, e‐jet printing is investigated for its capability in depositing multilayer thin‐films with microscale spatial resolution and nanoscale thickness resolution to create arrays of 1D photonic crystals (1DPC). In this paper, an empirical model for the deposition process is developed, relating process and material parameters to the thickness and uniformity of the patterns. Standard macroscale measurements of solid surface energy and liquid surface tension are used in conjunction with microscale contact angle measurements to understand the length scale dependence of material properties and their impact on droplet merger into uniform microscale thin‐films. The model is validated with several photopolymer inks, a subset of which is used to create pixelated, multilayer arrays of 1DPCs with uniformity and resolution approaching standards in the optics manufacturing industry. It is found that the printed film topography at the microscale can be predicted based on the surface energetics at the microscale. Due to the flexibility in design provided by the e‐jet process, these findings can be generalized for fabricating additional multimaterial, multilayer micro‐ and nanostructures with applications beyond the field of optics.

 

Additive manufacturing systems that can arbitrarily deposit multiple materials into precise, 3D spaces spanning the micro‐ to nanoscale are enabling novel structures with useful thermal, electrical, and optical properties. In this companion paper set, electrohydrodynamic jet (e‐jet) printing is investigated for its ability in depositing multimaterial, multilayer films with microscale spatial resolution and nanoscale thickness control, with a demonstration of this capability in creating 1D photonic crystals (1DPCs) with response near the visible regime. Transfer matrix simulations are used to evaluate different material classes for use in a printed 1DPC, and commercially available photopolymers with varying refractive indices (n= 1.35 to 1.70) are selected based on their relative high index contrast and fast curing times. E‐jet printing is then used to experimentally demonstrate pixelated 1DPCs with individual layer thicknesses between 80 and 200 nm, square pixels smaller than 40 µm across, with surface roughness less than 20 nm. The reflectance characteristics of the printed 1DPCs are measured using spatially selective microspectroscopy and correlated to the transfer matrix simulations. These results are an important step toward enabling cost‐effective, custom‐fabrication of advanced imaging devices or photonic crystal sensing platforms.