Data-Driven Origami Mechanism Design Based on Machine Learning Modeling and Optimization
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Abstract
Origami structures, due to their light weight, foldability, and reconfigurability, have broad application potential in robotics, aerospace devices, and medical instruments. This study proposes a data-driven origami mechanism design method based on machine learning. A training dataset containing 20,000 sets of geometric parameters and kinematic performance was constructed, and a deep neural network was applied to build the input-output mapping, enabling fast prediction of origami mechanism performance. Experimental results showed that the average error in predicting folding angle and unfolding stiffness was within 3%, while the computational speed was about 15 times faster than finite element analysis. By further combining genetic algorithms with reinforcement learning, the optimized design improved load-bearing capacity by 28% and increased unfolding efficiency by 22%. This study provides a new approach for the rapid design and engineering application of complex origami structures.
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