A machine learning approach to fracture mechanics problems. (15th May 2020)

• Record Type: Journal Article
• Authors: Liu, Xing; Athanasiou, Christos E.; Padture, Nitin P.; Sheldon, Brian W.; Gao, Huajian
• Is Part Of: Acta materialia. Volume 190(2020)
• Page Start: 105
• Page End: 112
• Publication Date: 2020-05-15
• Subjects: Machine learning -- Analytical methods -- Mechanical properties testing -- Fracture; Materials -- Periodicals; Materials science -- Periodicals; Materials -- Mechanical properties -- Periodicals; Metallurgy -- Periodicals; Chemistry, Inorganic -- Periodicals; 620.112
• DOI: 10.1016/j.actamat.2020.03.016
• Languages: English
• ISSNs: 1359-6454

Abstract

Abstract: Analytical and empirical solutions to engineering problems are usually preferred because of their convenience in applications. However, they are not always accessible in complex problems. A new class of solutions, based on machine learning (ML) models such as regression trees and neural networks (NNs), are proposed and their feasibility and value are demonstrated through the analysis of fracture toughness measurements. It is found that both solutions based on regression trees and NNs can provide accurate results for the specific problem, but NN-based solutions outperform regression-tree-based solutions in terms of their simplicity. This example demonstrates that ML solutions are a major improvement over analytical and empirical solutions in terms of both reliable functionality and rapid deployment. When analytical solutions are not available, the use of ML solutions can overcome the limitations of empirical solutions and substantially change the way that engineering problems are solved. Graphical abstract: Image, graphical abstract