Statistical properties of ridge networks in crumpled sheets

We study a folding model of crumpled paper using a flat folding and d-fold algorithm introduced by Hofmann, et al. (2019). The progression of ridge length and angle distributions obtained from the model and complementary experiments are analyzed in terms of crumples generated with random flat-fold or d-cone seeded algorithms. The ridge length distribution and scaling of number of vertices and edges of the domains enclosed is further compared with complementary sequential folding-unfolding algorithms which also induces creases. The number of edges of the domains is found to saturate with increasing number of folds in all cases. We then demonstrate that patterns generated by the two algorithms can be distinguished using convolution neural network (CNN) classification scheme.