Learning the Buckled Geometry of 3D Printed Stiffeners of Pre-Stretched Soft Membranes

A deep learning strategy was exploited to learn and predict the deformation of stiffeners, 3D printed onto a pre-stretched soft membrane. The working process reads as follows: the membrane is stretched until a pre-defined level; a specific geometry of stiffeners is printed onto it; the membrane is finally released, and due to the presence of the printed stiffeners, the system undergoes an out-of-plane deformation due to buckling. Fused deposition modeling was specifically calibrated to print PLA (Polylactic acid or polylactide) on a Lycra fabric. To assess how the printed pattern affects the buckled configuration, samples featuring different dimensions and in-plane geometries of the stiffeners were printed and numerically modeled via finite elements (FEs). The calibrated model was next exploited to construct a larger training dataset of stiffener geometries. A pre-trained You Only Look Once (YOLO)-based digital model was finally trained to foresee the link between the in-plane dimensions of the stiffeners before the release and the out-of-plane displacements in the buckled configuration. By handling around 100 different patterns, a precision of 93% in terms of recognition of the in-plane dimensions of the stiffeners and a mean absolute percentage error of 5% at most in terms of an estimate of the features of the buckled configuration were attained. The reported results testify the capability of the proposed approach and its potential efficiency to optimize the shape of the 3D printed geometries.