A machine learning approach to predicting mechanical behaviour of non-rigid foldable square-twist origami. (1st March 2023)

• Record Type: Journal Article
• Authors: Zhang, Dian; Qin, A.K.; Chen, Yan; Lu, Guoxing
• Is Part Of: Engineering structures. Volume 278(2023)
• Publication Date: 2023-03-01
• Subjects: Non-rigid foldable origami -- Machine learning -- Mechanical behaviours -- Dimensional analysis; Structural engineering -- Periodicals; Structural analysis (Engineering) -- Periodicals; Construction, Technique de la -- Périodiques; Génie parasismique -- Périodiques; Pression du vent -- Périodiques; Earthquake engineering; Structural engineering; Wind-pressure; Periodicals; 624.105
• DOI: 10.1016/j.engstruct.2022.115497
• Languages: English
• ISSNs: 0141-0296

Abstract

Highlights: A machine learning approach has been firstly used in predicting the mechanical responses of origami structures, which is difficult to investigate analytically or even empirically. The Decision Tree and MLP based solutions presented satisfactory results for the transition point, maximum strain energy, and energy barrier of the square-twist origami structure, even in the case of a particularly small dataset. Dimensional analysis added to this machine learning approach can reduce the computational time and increase the accuracy of the predicted results. Abstract: Origami-inspired metamaterials offer unlimited possibilities and broad application prospects in science and engineering. In particular, non-rigid foldable origami structures potentially provide more favorable characteristics than rigid foldable ones owing to the flexibility of thin sheets. However, the previous rigid analytical solutions are not sufficient to reflect the actual motions of non-rigid origami because of facet deformation, which prevents the widespread promotion of non-rigid origami applications. In this study, a machine learning approach has been one of first proposed for predicting the mechanical behaviours of a non-rigid foldable square-twist origami pattern (Type 1). Decision tree and multilayer perceptron were applied to classify multi-stability and predict the transition point, maximum strain energy, and energy barrier of the square-twist origami structure. Both methods efficiently provided accurate results for various geometric and material parameters. It is worth mentioning that the dimensional analysis added to this approach can reduce the computational time and increase the accuracy of the predicted results. Due to the same folding principle and similar parameters of origami patterns, the machine learning approach proposed herein is a promising alternative for other complex origami engineering problems when analytical and empirical solutions are unavailable.