Optimizing Stimuli-Based 4D Printed Structures: a Paradigm Shift in Programmable Material Response

This paper introduces a methodology for optimizing 4D printing design through the integration of Residual Neural Network (ResNet) and Genetic Algorithms (GA). Departing from traditional forward design approaches, our inverse design methodology addresses both the forward prediction and inverse optimization problems. ResNet efficiently predicts the performance of 4D-printed parts given their design, while GA optimizes material allocation and stimuli distribution to achieve desired configurations. The ResNet model exhibits high accuracy, converging to a small error (10-3), as validated across diverse cases. The GA demonstrates effectiveness in achieving optimal or near-optimal solutions, illustrated through case studies shaping parts into a parabola and a sinusoid. Experimental results align with optimized and simulated outcomes, showcasing the practical applicability of our approach in 4D printing design optimization.