ABSTRACT
Recent advances in material science and manufacturing have enabled designers to create objects which respond to changing environmental conditions by controlled deformation, without external mechanical or electrical actuation. The design of such smart materials has mostly been done through trial and error due to their complex nonlinear behavior. This paper will present how this problem is addressed by introducing a novel inverse design workflow. In this, a desired structural deformation is used as an input to a machine learned model which subsequently outputs the required geometric and material properties that will produce said deformation when exposed to an external stimulus. This workflow uses a Generative Adversarial Neural Network (GANN) trained on pairs of input cut-out patterns of laminate layers and their nonlinear Finite Element Analysis (FEA) simulation results. The method offers a significant performance speed-up while maintaining acceptable levels of accuracy, especially at the early design stage. This methodology could be further extended to the design of any nonlinear mechanical deformation.
