Enhancing the efficiency of 4D printing design through time-series prediction by dense-LSTM crossed network

This paper proposes a deep learning-based approach to predict the deformation of 4D-printed hydrogel models with varying lengths, aiming to improve the efficiency of the design process. A voxel-based modeling method is used to create hydrogel models in Abaqus, and their deformation data is obtained through finite element analysis (FEA). A mixed dataset is then constructed by mapping each model's expansion rate sequence to its corresponding deformation outcome. Based on this dataset, a novel deep learning architecture called dense-LSTM crossed network (DSCN) is introduced and trained. The trained model enables direct prediction of deformation results from the initial model parameters, reducing reliance on time-consuming simulation. Experimental results show that the proposed method shortens the model design verification process by 1.5–2%, thus enhancing the overall efficiency of 4D printing design. This study demonstrates the potential of combining intelligent modeling with deep learning to streamline additive manufacturing workflows involving shape-morphing materials.