Integrated simulation, machine learning, and experimental approach to characterizing fracture instability in indentation pillar-splitting of materials

Measuring fracture toughness of materials at small scales remains challenging due to limited experimental testing configurations. A recently developed indentation pillar-splitting method has shown promise of improved flexibility in fracture toughness measurements at the microscale, partly due to the occurrence of an unusual fracture instability, i.e., a transition from stable to unstable crack propagation. In spite of growing interest in this method, the underlying mecha-nism of this phenomenon is yet to be elucidated. Here, we provide a comprehensive description of fracture instability in indentation pillar-splitting by combining in situ experiments with high-fidelity simulations based on cohesive zone and J-integral methods. In addition, a machine -learning-based solution for predicting the critical indentation load of fracture instability is established through Gaussian processes regression for broad use of this method by the community.

Automated summary

Measuring fracture toughness of materials at small scales remains challenging, but the recently developed indentation pillar-splitting method shows promise in improving flexibility for such measurements at the microscale. However, the underlying mechanism of the fracture instability observed in this method is still unclear. In this study, in situ experiments and high-fidelity simulations were combined to provide a comprehensive description of the fracture instability in indentation pillar-splitting. Additionally, a machine-learning-based solution for predicting the critical indentation load of fracture instability was established for broader use of this method by the community.