3D Printing of Hierarchical Fiber-Reinforced Soft-Composites

分层纤维增强软复合材料是由嵌入软聚合物基体中的聚合物纤维（具有特定的材料性能和分层长度尺度）组成的复合材料。 它们的新兴应用突出了将联合收割机3D打印（或增材制造）技术与聚合物纤维网络的定向沉积相结合的迫切需要。虽然使用液滴沉积或熔融挤出技术打印多材料聚合物部件是成熟的，但分层纤维增强软复合材料的3D打印在很大程度上尚未得到证实。该奖项支持针对这些复合材料开发3D打印工艺的基础研究。研究结果将为此类复合材料开发可靠且可扩展的3D打印技术，从而为3D打印机创造新的细分市场。 它还将促进生物启发材料、嵌入式传感和4D打印（具有形状记忆特性的3D打印产品）等领域的创新。这些途径共同代表了一个数十亿美元的全球市场，影响着航空航天、医疗保健和国防等关键行业。该奖项还支持教育推广活动，这些活动将影响多个机构（包括伦斯勒理工学院和哈德逊谷社区学院）的学生以及纽约北部地区的少数民族服务中学/高中。这项研究将调查分层纤维增强软复合材料3D打印所特有的基础制造科学和过程控制问题。PI最近发明了一种新型3D打印机，该打印机将紫外线固化聚合物的传统喷墨打印与静电纺丝纤维网络的直接冲压相结合。使用这个3D打印平台，PI将研究关键纤维和基材特性对纤维转移效率的影响以及3D打印过程中遇到的纤维-液滴相互作用的物理特性。在实验方面，该研究将专注于制造适用于3D打印的各种图案化纤维，然后进行涉及纤维转移效率/材料特性表征的工艺研究以及纤维-液滴相互作用的高速成像研究。本研究的建模方面将专注于建立为3D打印分层纤维增强软复合材料量身定制的新型控制策略。工艺研究将产生定量指标，将纤维转移效率映射到关键工艺参数，如冲压载荷和纤维/基材性能。这些工艺图将使您能够在控制3D打印过程的同时选择适当的冲压负载曲线。高速成像研究预计将确定分层纤维增强软复合材料3D打印过程中遇到的新液滴扩散机制，液滴形状和特征时间尺度。

Hierarchical fiber-reinforced soft-composites are composites made up of polymeric fibers (with specific material properties and hierarchical length-scales) embedded within a soft polymer matrix. Their emerging applications have highlighted the impending need to combine 3D printing (or additive manufacturing) technologies with the directed deposition of polymeric fiber networks. While the printing of multi-material polymer parts using droplet-deposition or melt-extrusion techniques is well-established, the 3D printing of hierarchical fiber-reinforced soft-composites is largely unproven. This award supports fundamental research directed towards developing 3D printing processes for these composites. The research findings will enable the development of a reliable and scalable 3D printing technology for such composites, leading to the creation of a new market-segment for 3D printers. It will also facilitate innovations in fields such as bio-inspired materials, embedded sensing, and 4D printing (3D printed products with shape-memory properties). These avenues collectively represent a multi-billion dollar world-wide market that affects key industries such as aerospace, healthcare, and defense. This award also supports educational outreach activities that will impact students in multiple institutions (including Rensselaer Polytechnic Institute and Hudson Valley community college) and the minority-serving middle/high-schools in the upstate New York area.This research will investigate the fundamental manufacturing science and process-control problems unique to the 3D printing of hierarchical fiber-reinforced soft-composites. The PIs have recently invented a novel 3D printer that combines the conventional inkjet-based printing of ultraviolet curable polymers with the directed stamping of electrospun fiber networks. Using this 3D printing platform, the PIs will investigate the effect of key fiber and substrate properties on the fiber-transfer efficiencies and the physics of the fiber-droplet interactions encountered during the 3D printing process. On the experimental front, the research will focus on the manufacture of a diverse family of patterned fibers suitable for 3D printing, followed by process studies involving fiber-transfer efficiencies/material property characterization and high-speed imaging studies of fiber-droplet interactions. The modeling aspect of this research will focus on establishing novel control strategies tailored for 3D printing hierarchical fiber-reinforced soft-composites. The process studies will yield quantitative metrics that map the fiber-transfer efficiencies to critical process parameters such as stamping load, and fiber/substrate properties. These process maps will enable the selection of appropriate stamping load profiles while controlling the 3D printing process. The high-speed imaging study is expected to identify new droplet spreading regimes, droplet shapes and characteristic time-scales encountered during the 3D printing of hierarchical fiber-reinforced soft-composites.