Machine learning-based morphological and mechanical prediction of kirigami-inspired active composites

Kirigami-inspired designs hold great potential for the development of functional materials and devices, but predicting the morphological configuration of these structures under various loading conditions remains a challenge for traditional experimental and numerical methods. Here, we present a novel approach that utilizes machine learning algorithms to accurately predict the deformation and stress field of kirigami-inspired programmable active composites. To train our model, first, we used a chemical corrosion algorithm to generate a dataset of kirigami-inspired imaging model accompanied by utilizing finite element simulations to obtain their deformation and stress fields as the ground truth, and subsequently trained the machine learning model to offer robust predictions of the displacement and stress fields of the designated structures. The graphically preprocessing transformation between color space and deformation(stress) fields is used to match the fields prediction of mechanical problems with powerful machine learning approaches in image processing. Our results demonstrate the effectiveness of this approach in predicting the morphological and mechanical behaviors of kirigami-inspired active structures, paving the way for the development of advanced and functional composite designs that are programmable and active.