Machine learning based inverse design of complex microstructures generated via hierarchical wrinkling

Abstract

Hierarchical wrinkling is a buckling-based low-cost technique for fabrication of complex microstructures over large areas. However, predicting and fabricating the desired microstructures is challenging due to the underlying nonlinear mechanics. Although computational predictive tools based on finite elements analysis (FEA) are available, these are impractical for inverse design, i.e., for the prediction of inputs that would generate the desired wrinkles. This is because one must resort to slow and resource-intensive iterations to predict the inputs. Here, we present a prediction technique that is based on machine learning (ML) to overcome this limitation. Our technique comprises: (i) capturing the predictive capability of FEA through computationally-efficient surrogate ML models, (ii) applying the models to map the feasible input design space into the outputs, i.e., the predicted wrinkles, and (iii) searching through the predicted wrinkles for the desired microstructure. We have validated our technique against FEA simulations and have demonstrated its utility by rapidly identifying the inputs that would generate desired hierarchical wrinkles including flat-top sinusoidal patterns. Our approach shortens the exploration and comparison of a million design choices from more than ten days to less than a minute. It can therefore significantly mature wrinkling into a scalable manufacturing process for optics, sensing, and surface texturing applications.