Mapping material-property space of cellular metamaterials under uncertainty

Abstract

Cellular mechanical metamaterials (CMMs) are assemblies of periodic representative volume elements which can be engineered to exhibit unique mechanical properties. Recent advances in additive manufacturing (AM) have enabled us to fabricate sophisticated architected materials with high precision. To increase and diversify applications in both science and engineering practice, the rapid development of fabrication technologies necessities a novel and effective algorithm to outline a theoretical property space for material design problems, allowing designers to acknowledge the limitations of material performance, and to make informed trade-off between target objectives and competing industrial requirements. Inspired by biological evolution, the proposed work addresses a methodology for intuitively mapping material-property spaces of CMMs by using the genetic algorithm as a sampling algorithm, consisting selection of objective properties and stochastic search of property points. It approximates the property space more faithfully and comprehensively than traditional mapping approaches. Considering the manufacturing defects of the AM, uncertainties in properties are quantified via the deep learning method. Variations of properties induced by the defects are illustrated as stochastic boundaries of the property space.