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.

Emerging polymer 3D-printing technologies are enabling the design and fabrication of mechanically efficient lattice structures with intricate microscale structures. During fabrication, manufacturing inconsistencies can affect mechanical efficiency, thereby driving a need to investigate how design and processing strategies influence outcomes. Here, mechanical testing is conducted for 3D-printed lattice structures while altering topology, relative density, and exposure time per layer using digital light processing (DLP). Experiments compared a Cube topology with 800 µm beams and Body-Centered Cube (BCC) topologies with 500 or 800 µm beams, all designed with 40% relative density. Cube lattices had the lowest mean measured relative density of ~42%, while the 500 µm BCC lattice had the highest relative density of ~55%. Elastic modulus, yield strength, and ultimate strength had a positive correlation with measured relative density when considering measurement distributions for thirty samples of each design. BCC lattices designed with 50%, 40%, and 30% relative densities were then fabricated with exposure-per-layer times of 1500 and 1750 ms. Increasing exposure time per layer resulted in higher scaling of mechanical properties to relative density compared to design alteration strategies. These results reveal how design and fabrication strategies affect mechanical performance of lattices suitable for diverse engineering applications.

Computational design is growing in necessity for advancing biomedical technologies, particularly for complex systems with numerous trade-offs. For instance, in tissue scaffolds constructed from repeating unit cells, the structure’s porosity and topology affect biological tissue and vasculature growth. Here, we adapt curvature-based tissue growth and agent-based vasculature models for predicting scaffold mechanobiological growth. A non-dominated sorting genetic algorithm (NSGA-II) is used for dual-objective optimization of scaffold tissue and blood vessel growth with heterogeneous unit cell placement. Design inputs consist of unit cells of two different topologies, void unit cells, and beam diameters from 64 to 313 µm. Findings demonstrate a design heuristic for optimizing scaffolds by placing two selected unit cells, one that favors high tissue growth density and one that favors blood vessel growth, throughout the scaffold. The pareto front of solutions demonstrates that scaffolds with large porous areas termed channel voids or small voids improve vasculature growth while lattices with no larger void areas result in higher tissue growth. Results demonstrate the merit in computational investigations for characterizing tissue scaffold design trade-offs and provide a foundation for future design multi-objective optimization for complex biomedical systems.

Internal mixing twin-fluid atomiser is known to be one of the prominent atomisers for the generation of spray or spray-like dispersion from bulk liquids. This paper designs and investigates the performance of a customised liquid siphon type internal mixing twin-fluid atomiser for the generation of test aerosol particles from oleic acid in an ethyl alcohol solution. Experimental results show the effect of airflow rate on suction height and liquid consumption rate of the atomiser with different design parameters. Variation of suction height and liquid consumption rate are investigated for six different design atomisers by varying the airflow rate between 18 and 230 lpm. It is seen that the atomiser with optimum design can have a suction height of up to 103 cm and a liquid consumption rate of up to 1545 ml/h. Atomiser 3 (with air inlet diameter = 18 mm and liquid jet diameter = 2 mm) produces better test aerosol participles with constant S/D = 0.66 and 10% solution concentration. While using this atomiser for the production of test aerosol, the lowest geometric standard deviation found in the experiment was 1.2.

*The study and application of computational design is gaining importance in biomedical engineering as medical devices are becoming more complex, especially with the emergence of 3D printed scaffold structures. Scaffolds are medical devices that act as temporary mechanical support and facilitate biological interactions to regenerate damaged tissues. Past computational design studies