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

Abstract

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

Keywords

• deep learning
• inverse structure design
• meta‐fiber
• meta‐fiber reinforced hydrogel
• targeted mechanical field