Fast Reverse Design of 4D-Printed Voxelized Composite Structures Using Deep Learning and Evolutionary Algorithm.

ABSTRACT: 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 suppo

SUBMITTER: Wang M 

PROVIDER: S-EPMC11948029 | biostudies-literature | 2025 Mar

REPOSITORIES: biostudies-literature