Prediction and design of mechanical properties of origami-inspired braces based on machine learning

Abstract

In order to rapidly and accurately evaluate the mechanical properties of a novel origami-inspired tube structure with multiple parameter inputs, this study developed a method of designing origami-inspired braces based on machine learning models. Four geometric parameters, i.e., cross-sectional side length, plate thickness, crease weakening coefficient, and plane angles, were used to establish a mapping relationship with five mechanical parameters, including elastic stiffness, yield load, yield displacement, ultimate load, and ultimate displacement, all of which were calculated from load-displacement curves. Firstly, forward prediction models were trained and compared for single and multiple mechanical outputs. The parameter ranges were extended and refined to improve the predicted results by introducing the intrinsic mechanical relationships. Secondly, certain reverse prediction models were established to obtain the optimized design parameters. Finally, the design method of this study was verified in finite element methods. The design and analysis framework proposed in this study can be used to promote the application of other novel multi-parameter structures.