Polymer 3D printing is an emerging technology with recent research translating towards increased use in industry, particularly in medical fields. Polymer printing is advantageous because it enables printing low-cost functional parts with diverse properties and capabilities. Here, we provide a review of recent research advances for polymer 3D printing by investigating research related to materials, processes, and design strategies for medical applications. Research in materials has led to the development of polymers with advantageous characteristics for mechanics and biocompatibility, with tuning of mechanical properties achieved by altering printing process parameters. Suitable polymer printing processes include extrusion, resin, and powder 3D printing, which enable directed material deposition for the design of advantageous and customized architectures. Design strategies, such as hierarchical distribution of materials, enable balancing of conflicting properties, such as mechanical and biological needs for tissue scaffolds. Further medical applications reviewed include safety equipment, dental implants, and drug delivery systems, with findings suggesting a need for improved design methods to navigate the complex decision space enabled by 3D printing. Further research across these areas will lead to continued improvement of 3D-printed design performance that is essential for advancing frontiers across engineering and medicine.

Engineering structures and infrastructure continue to be used despite approaching or having reached their design lifetime. While contact-based measurement techniques are challenging to implement at a large scale and provide information at discrete locations only, non-contact methods are more user-friendly and offer accurate, robust, and continuous spatial information to quantify the structural conditions of the targeted systems. Advancements in optical sensors and image-processing algorithms increased the applicability of image-based, non-contact techniques such as photogrammetry, infrared thermography, and laser imaging for structural health monitoring (SHM). Additionally, with the incorporation of artificial intelligence (AI) algorithms, the assessment process is expedited and made more efficient. This paper summarizes the efforts made in the last five years to leverage AI-aided, non-contact sensing techniques for applications in SHM with emphasis on image-based methods. Future directions to advance AI-aided image-based sensing techniques for SHM of engineering structures are also discussed.

The use of three-dimensional (3D) printing for lattice structures has led to advances in diverse applications benefitting from mechanically efficient designs. Three-dimensional printed lattices are often used to carry loads, however, printing defects and inconsistencies potentially hinder performance. Here, we investigate the design, fabrication, mechanics, and reliability of lattices with repeating cubic unit cells using probabilistic analysis. Lattices were designed with 500 μm diameter beams and unit cell lengths from 0.8 mm to 1.6 mm. Designs were printed with stereolithography and had average beam diameters from 509 μm to 622 μm, thereby demonstrating a deviation from design intentions. Mechanical experiments were conducted and demonstrated an exponential increase in yield stress for lattice relative density that facilitated probabilistic failure analysis. Sensitivity analysis demonstrated lattice mechanics were most sensitive to fluctuations for beam diameter (74%) and second to lattice yield stress (8%) for lattices with 1.6 mm unit cells, while lattices with smaller 1.0 mm unit cells were most sensitive to yield stress (48%) and second to beam diameter (43%). The methodological framework is generalizable to further 3D printed lattice systems, and findings provide new insights linking design, fabrication, mechanics, and reliability for improved system design that is crucial for engineers to consider as 3D printing becomes more widely adopted.