Wang Mengtao, Liu Zaiyang, Furukawa Hidemitsu, Li Zhuo, Ge Yifei, Xu Yifan, Qiu Zhe, Tian Yang, Wang Zhongkui, Xu Ren, Meng Lin
Department of Electronic and Computer Engineering, Ritsumeikan University, Shiga, 525-8577, Japan.
Department of Robotics, Ritsumeikan University, Shiga, 525-8577, Japan.
Adv Sci (Weinh). 2025 Mar;12(12):e2407825. doi: 10.1002/advs.202407825. Epub 2025 Feb 1.
Designing voxelized composite structures via 4D printing involves creating voxel units with distinct material properties that transform in response to stimuli; however, optimally distributing these properties to achieve specific target shapes remains a significant challenge. This study introduces an optimization method combining deep learning (DL) and an evolutionary algorithm, focusing on a solvent-responsive hydrogel as the target material. A sequence-enhanced parallel convolutional neural network is developed and generated a dataset through finite element simulations. This DL model enables high-precision prediction of hydrogel deformation. Furthermore, a progressive evolutionary algorithm (PEA) is proposed by integrating the DL model to construct a DL-PEA framework. This framework supports rapid reverse engineering of the desired shape, and the average design time for specified target shapes is reduced to ≈3.04 s. The present findings illustrate how 4D printing of optimized hydrogel designs can effectively transform in response to environmental stimuli. This work provides a new perspective on the application of hydrogels in 4D printing and presents an efficient tool for optimizing 4D-printed voxelized composite structures.
通过4D打印设计体素化复合结构涉及创建具有不同材料特性的体素单元，这些特性会根据刺激发生转变；然而，以最佳方式分布这些特性以实现特定目标形状仍然是一项重大挑战。本研究引入了一种将深度学习（DL）与进化算法相结合的优化方法，重点关注一种对溶剂有响应的水凝胶作为目标材料。开发了一种序列增强并行卷积神经网络，并通过有限元模拟生成了一个数据集。这个DL模型能够高精度预测水凝胶的变形。此外，通过整合DL模型提出了一种渐进进化算法（PEA），以构建一个DL-PEA框架。该框架支持对所需形状进行快速逆向工程，将指定目标形状的平均设计时间缩短至约3.04秒。本研究结果说明了优化水凝胶设计的4D打印如何能够有效地响应环境刺激而发生转变。这项工作为水凝胶在4D打印中的应用提供了新的视角，并为优化4D打印体素化复合结构提供了一种有效的工具。
