Machine learning driven forward prediction and inverse design for 4D printed hierarchical architecture with arbitrary shapes

Abstract

The forward prediction and inverse design of 4D printing have primarily focused on 2D rectangular surfaces or plates, leaving the challenge of 4D printing parts with arbitrary shapes underexplored. This gap arises from the difficulty of handling varying input sizes in machine learning paradigms. To address this, we propose a novel machine learning-driven approach for forward prediction and inverse design tailored to 4D printed hierarchical architectures with arbitrary shapes. Our method encodes non-rectangular shapes with special identifiers, transforming the design domain into a format suitable for machine learning analysis. Using Residual Networks (ResNet) for forward prediction and evolutionary algorithms (EA) for inverse design, our approach achieves accurate and efficient predictions and designs. The results validate the effectiveness of our proposed method, with the forward prediction model achieving a loss below 10−2 mm, and the inverse optimization model maintaining an error near 1 mm, which is low relative to the entire shape of the optimized model. These outcomes demonstrate the capability of our approach to accurately predict and design complex hierarchical structures in 4D printing applications