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.

Human-based segmentation of tomographic images can be a tedious time-consuming task. Deep learning algorithms and, particularly, convolutional neural networks have become state of the art techniques for pattern recognition in digital images that can replace human-based image segmentation. However, their use in materials science is beginning to be explored and their application needs to be adapted to the specific needs of this field. In the present work, a convolutional neural network is trained to segment the microstructural components of an Al-Si cast alloy imaged using synchrotron X-ray tomography. A pixel-wise weighted error function is implemented to account for microstructural features which are hard to identify in the tomographs and that play a relevant role for the correct description of the 3D architecture of the alloy investigated. The results show that the total operation time for the segmentation using the trained convolutional neural network was reduced to <1% of the time needed with human-based segmentation.

Digital image correlation (DIC) is a technique in experimental mechanics to acquire full-field measurement data of displacements and deformations from the surface of specimens or components. Especially for the investigations of cracks it provides additional benefits. The actually present deformation field in the vicinity of the crack tip can be obtained which directly reflects for example crack closure effects or plasticity. Against this background the paper summarizes a procedure to compute the J integral and the stress intensity factors K_I and K_II based on DIC data. For this purpose the J and interaction integral are computed as line and domain integrals. Through experiments it is shown that the domain integral is less affected by scatter of the DIC data. Furthermore, the specific domain, facet sizes and facet distances slightly influence the results.

The process–microstructure–property relationship of high-strength 7000 series aluminum alloys during fatigue crack propagation (FCP) is highly relevant for safety during the design and service of aircraft structural components. It is scientifically evident that many metallurgical factors affect FCP properties, but partly contradictory or inconclusive results show that the quantitative description of the relationships is still a major challenge among researchers and engineers. Most research focuses on sheet or plate products and investigations lack quantitative information on the process–property relationship between open-die forged thick products and FCP. The present study contributes to this field by investigating the fatigue crack growth behavior of an open-die forged AA7010-T7452 aluminum alloy. Four different forging conditions comprising different characteristic microstructures are comparatively analyzed. The influence of grain size, grain shape, specimen orientation, crystallographic texture, and primary phase particles is investigated. Fractographic analysis reveals different active damage mechanisms during fatigue crack growth. Based on that, the microstructure features relevant to fatigue damage areidentified in each regime of crack growth.

Today’s societal challenges require rapid response and smart materials solutions in almost all technical areas. Driven by these needs, data-driven research has emerged as an enabler for faster innovation cycles. In fields such as chemistry, materials science and life sciences, autonomous data generation and processing is already accelerating knowledge discovery. In contrast, in experimental mechanics, complex investigations like studying fatigue crack growth in structural materials have traditionally adhered to standardized procedures with limited adoption of the digital transformation. In this work, we present a novel infrastructure for data-centric experimental mechanics. The setup is demonstrated using a complex fatigue crack growth experiment for aerospace materials. Our methodology incorporates an open-source Python library that complements a multi-scale digital image correlation and robot-assisted test rig. Our novel approach significantly increases the information-to-cost ratio of fatigue crack growth experiments in aerospace materials compared to traditional experiments. Thus, serves as a catalyst for discovering new scientific knowledge and contributes to the data-driven acceleration of the deployment of new applications in the field of structural materials and structures.