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

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.

Fatigue modeling using neural networks: A comprehensive review

“…Artificial data can also be generated from physics models. In the work of Strohmann et al, 164 the training data are augmented with data from finite element analysis. In Belfiore et al, 182 new data are created from a theoretical model.…”

“…Strohmann et al 164 used a U‐net type CNN for crack segmentation. The goal is to classify the crack path, crack tip, and background in an image.…”

Materials Informatics for Mechanical Deformation: A Review of Applications and Challenges

“…Furthermore, ML methods have been applied to determine the fracture toughness of composite materials based on DIC results, typically using artificial neural networks (ANNs) [ 45 , 61 , 62 ]. Crack detection, measurement, and characterization based on DIC can be performed using image processing methods [ 63 ] and fatigue crack detection in DIC images may be automatically performed using CNNs [ 64 ].…”

Artificial intelligence‐assisted fatigue fracture recognition based on morphing and fully convolutional networks

“…10,11 Meanwhile, AI-based methods were also studied to process and classify the fracture surface based on fuzzy logic, wavelets, convolutional neural networks (CNNs), and so on. 8,[12][13][14][15][16] However, the overall analysis of fatigue fracture with area recognition of fatigue crack propagation and fast fracture regions is still under development, and the dataset scale is limited. Kalayci et al 17 systemically concluded the recent development of fatigue life estimation, pointing out that AI systems shall play an important role in the near future for fatigue analysis.…”