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Circular supply chains require more accurate product labeling and traceability. In the apparel industry, product life cycle management is hampered in part by inaccurate, poorly readable, and detachable standard care labels. Instead, this article seeks to enable a labeling system capable of being integrated into the fabric itself, intrinsically recyclable, low‐cost, encodes information, and allows rapid readout after years of normal use. In this work, all‐polymer photonic crystals are designed and then fabricated by thermal drawing with >100 layers having sub‐micrometer individual thickness and low refractive index contrast (Δn = 0.1). The fibers exhibit reflectance features in the 1–5.5 µm wavelength range, characterized using insitu Fourier transform infrared spectroscopy. Drawn photonic fibers are then woven into fabrics, characterized by near‐infrared spectroscopy and short‐wave infrared imaging, techniques commonly used in industrial facilities for sorting materials. The fibers’ optical design also enables the use of overtone peaks to avoid overlap with parasitic molecular absorption, substantially improving the signal‐to‐noise ratio (and therefore ease and speed) of readout. The ability to produce kilometers of fiber that are compatible with existing textile manufacturing processes, coupled with low input material cost, make these a potential market‐viable improvement over the standard care label.

This paper studies the concept of manufacturing systems that autonomously learn how to build parts to a user-specified performance. To perform such a function, these manufacturing systems need to be adaptable to continually change their process or design parameters based on new data, have inline performance sensing to generate data, and have a cognition element to learn the correct process or design parameters to achieve the specified performance. Here, we study the cognition element, investigating a panel of supervised and reinforcement learning machine learning algorithms on a computational emulation of a manufacturing process, focusing on machine learning algorithms that perform well under a limited manufacturing, thus data generation, budget. The case manufacturing study is for the manufacture of an acoustic metamaterial and performance is defined by a metric of conformity with a desired acoustic transmission spectra. We find that offline supervised learning algorithms, which dominate the machine learning community, require an infeasible number of manufacturing observations to suitably optimize the manufacturing process. Online algorithms, which continually modify the parameter search space to focus in on favorable parameter sets, show the potential to optimize a manufacturing process under a considerably smaller manufacturing budget.

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.