Mechanical Field Guiding Structure Design Strategy for Meta‐Fiber Reinforced Hydrogel Composites by Deep Learning

• DOI: 10.1002/advs.202310141
• Publication Date: 2024-03-23T18:59:57Z
• Authors: Chuanzhi Liu; Xingyu Zhang; Xia Liu; Qingsheng Yang
• Keywords: Fiber-reinforced composite; Material Design

Abstract Fiber‐reinforced hydrogel composites are widely employed in many engineering applications, such as drug release, and flexible electronics, with more mechanical properties than pure materials. Comparing to the strengthened by continuous fiber, meta‐fiber reinforced provides stronger individualized design ability of deformation patterns tunable stiffness, especially for elaborate applications joint, cartilage, organ. In this paper, a novel structure strategy based on deep learning algorithm is proposed achieve targeted properties, stress displacement fields. A solid mechanic model first developed construct dataset fiber distribution corresponding composite. Generative adversarial network (GAN) then trained characterize relationship between or field, distribution. The well‐trained GAN implemented composite under specific operation conditions. results show that method may efficiently predict satisfied confidence, has great potential delivery electronics.