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 ...
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