Towards High-Throughput, Small-Scale Fracture Investigations via Machine Learning

Investigating mechanical properties at small scales is a challenging endeavor. It requires sophisticated micro-/nano-scale experimental methods combined with laborious/time-intensive finite element computations. In this talk, a new framework for materials characterization at small scales using the latest developments in machine learning will be presented. This framework involves multi-fidelity deep learning and active learning methods limiting the need for finite element simulations. Its application for predicting the fracture toughness of microscale pentagonal cross-sectional ceramic cantilevers as well as micropillars will be showcased, demonstrating that it can significantly accelerate fracture toughness characterizations at small scales.