Integrated simulation, machine learning, and experimental approach to characterizing fracture instability in indentation pillar-splitting of materials. (January 2023)

• Record Type: Journal Article
• Authors: Athanasiou, Christos E.; Liu, Xing; Zhang, Boyu; Cai, Truong; Ramirez, Cristina; Padture, Nitin P.; Lou, Jun; Sheldon, Brian W.; Gao, Huajian
• Is Part Of: Journal of the mechanics and physics of solids. Volume 170(2023)
• Publication Date: 2023-01
• Subjects: Fracture mechanics, Fracture instability, Machine learning, Small-scale materials characterization -- Indentation pillar-splitting; Mechanics, Applied -- Periodicals; Solids -- Periodicals; Mechanics -- Periodicals; Mécanique appliquée -- Périodiques; Solides -- Périodiques; Mechanics, Applied; Solids; Periodicals; 531.05
• DOI: 10.1016/j.jmps.2022.105092
• Languages: English
• ISSNs: 0022-5096

Abstract

Abstract: 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 mechanism 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.