Automatic detection of fatigue crack paths using digital image correlation and convolutional neural networks

Fatigue & Fracture of Engineering Materials & Structures, vol. 44, pp. 1336–1348

Abstract

Abstract The occurrence of fatigue cracks is an inherent part of the design of engineering structures subjected to nonconstant loads. Thus, the accurate description of cracks in terms of location and evolution during service conditions is mandatory to fulfill safety‐relevant criteria. In the present work, we implement a deep convolutional neural network to detect crack paths together with their crack tips based on displacement fields obtained using digital image correlation. To this purpose, fatigue crack propagation experiments were performed for AA2024‐T3 rolled sheets using specimens with different geometries. Several hundred datasets were acquired by digital image correlation during the experiments. A part of the displacement data from one of the specimens was then used to train the neural network. The results show that the method can accurately detect the shape and evolution of the cracks in all specimens. Adding synthetic data generated by finite element analyses to the training step improved the accuracy for cracks with stress intensity factors that exceeded the range of the original training data.

Authors 4

1. Tobias Strohmann corresponding

   Deutsches Zentrum für Luft- und Raumfahrt e. V. (DLR)

   Affiliation as printed

   German Aerospace Center (DLR) Institute of Materials Research Cologne Germany

   German Aerospace Center (DLR), Institute of Materials Research, Cologne, Germany

2. Denis Starostin‐Penner

   Deutsches Zentrum für Luft- und Raumfahrt e. V. (DLR)

   Affiliation as printed

   German Aerospace Center (DLR) Institute of Materials Research Cologne Germany

   German Aerospace Center (DLR), Institute of Materials Research, Cologne, Germany

3. Eric Breitbarth

   Deutsches Zentrum für Luft- und Raumfahrt e. V. (DLR)

   Affiliation as printed

   German Aerospace Center (DLR) Institute of Materials Research Cologne Germany

   German Aerospace Center (DLR), Institute of Materials Research, Cologne, Germany

4. Guillermo Carlos Requena Aachen

   Deutsches Zentrum für Luft- und Raumfahrt e. V. (DLR) · RWTH Aachen University

   Affiliation as printed

   German Aerospace Center (DLR) Institute of Materials Research Cologne Germany

   Metallic Structures and Materials Systems for Aerospace Engineering RWTH Aachen University Aachen Germany

   German Aerospace Center (DLR), Institute of Materials Research, Cologne, Germany

   Metallic Structures and Materials Systems for Aerospace Engineering, RWTH Aachen University, Aachen, Germany