DEVELOPMENT OF POROUS COMPOSITE FILAMENT FOR ADDITIVE MANUFACTURING OF LIGHTWEIGHT COMPONENTS

摘要

Development of new feed materials is a challenge for fused filament fabrication (FFF) additive manufacturing (AM) methods. Characterization of each composition of polymer or composite material requires determining rheological properties as well as material properties at various temperatures for filament extrusion and 3D printing processes. Such characterization campaign requires considerable time and effort. In this work, viscoelastic properties of polymers and composites are determined at various temperatures and strain rates from a dynamic mechanical analysis test on a single specimen. A transform is used to extract the elastic modulus over various temperatures and strain rates. The transform allows converting viscoelastic properties to elastic properties. In addition, artificial neural network based machine learning methods are used to further reduce the characterization efforts required for developing feed materials. The methods allow testing one specimen at a select few combinations of temperature and loading frequencies to develop the elastic modulus map over temperature and strain rates to obtain the information required for extruding filament and conducting 3D printing. The method is validated on neat polymers such as high density polyethylene, graphene nanocomposites and hollow particle filled syntactic foams. These methods can accelerate the timeline for adoption of new polymers in the AM process and allow printing parts using specialty polymers.