A Comprehensive Review of 3D-Printed Shape Memory Polymers: Influence of Infill Design, Printing Parameters, and Applications
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Abstract
Smart materials with shape memory, known as shape memory polymers (SMPs), are able to return to a permanent shape after being deformed, when an external stimulus, such as heat, is applied. Fused deposition modeling (FDM/FFF) is now the primary additive manufacturing process to produce SMP-based complex architectural structures such as those needed for biomedical applications. While a critical and largely unexplored processing parameter for governing porosity, mechanical stiffness, energy absorption, shape fixity ratio (Rf) and shape recovery ratio (Rr), the infill design (pattern geometry/porosity/scaffold design) critically affects all of these factors. This critical review provides an overview of 198 references on: (1) fundamentals and classification of SMPs; (2) FDM processing parameters and its effect on microstructure and thermal stability; (3) quantitative relationships between infill design and mechanical/ shape memory properties; (4) TGA/DSC-based thermal characterization; (5) biomedical and 4D printing applications and (6) challenges and future prospects. Our conclusions show that the highest compressive strength is achieved at 0.1 mm layer thickness, 230°C printing temperature and 20 mm/s printing speed, while 0.2 mm/220°C/30 mm/s yields the best combination of mechanical properties (modulus and strength), energy absorption and cyclic shape memory. With respect to the scaffold design, OK shows the highest Rf (97.92%) while the WV shows the highest Rr (98.11%). Gyroid and honeycomb infill patterns are deemed the best for biomedical applications for their continuous porosity and isotropy.
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