Polymeric composites for powder-based additive manufacturing : materials and applications - Yuan, Shangqin (2020)
As one of the most important categories in the additive manufacturing (AM) field, powder-based techniques, such as selective laser sintering, electron beam melting and selective laser melting, utilize laser or electronic beams to selectively fuse polymeric, metallic, ceramic or composite powders layer-by-layer into desired products according to their computer-aided design models. With unique mechanical, thermal, electrical, biocompatible and fire-retardant properties, polymeric composite materials for powder-based AM have been attracting intensive research interests because of their potential for a wide variety of functional applications in aerospace, automobile, marine and offshore, medical and many other industries. This article provides a comprehensive review of the recent progress on polymeric composite materials, their powder preparation for AM, and functionalities and applications of their printed products. It begins with the introduction of thermoplastic polymers that have been used as the main matrices of the polymeric composites and various composite reinforcements such as metallic, ceramic, carbon-based fillers and polymer blends for strengthening and functionality purposes. Discussion is then made on the processes for manufacturing such polymeric composites into powder form, which include shear pulverization, solution-based methods and melt compounding methods, with a focus on their advantages, limitations and challenges in terms of their productivity and processibility as well as powder printability. Thereafter, the properties and functionalities of the printed products and their various intriguing applications particularly in biomedical (anatomical models, tissue engineering and drug delivery), aerospace, automobile, military, energy and environmental, acoustic devices and sports equipment are highlighted. Finally, this review is concluded with an outlook on polymeric composites for powder-based AM, new opportunities, major challenges and possible solutions.