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.

Laser Powder Bed Fusion (LPBF) of Ti-6Al-4V enables the manufacturing of complex parts for lightweight applications. The emerging microstructure in the LPBF process and thus the mechanical properties are defined by the thermal cycles, which are locally variable for complex geometries. Predictions of local mechanical properties by simulation would reduce the development time of new applications drastically but are today not possible on part scale, so new part applications must be qualified experimentally at great effort. In this study, representative geometry sections were transferred into a simplified sample shape to mechanically characterize different geometry-dependent microstructures. In areas exposed to comparatively increased heat input over time, a lamellar $\alpha$ + $\beta$ microstructure with $\beta$ fraction up to 20% was measured in contrast to the common martensitic ${\alpha }^{\prime }$ microstructure of LPBF-manufactured Ti-6Al-4V, resulting in reduced tensile strength and fatigue life. For the first time, a correlation was successfully established between ultimate tensile strength of multiple geometries and the corresponding temperature–time cycles. With reduced computational effort by use of simplifying assumptions in the simulation, this correlation model can theoretically be applied to the part level. This work has laid the foundation for the simulation-based prediction of mechanical properties for entire parts manufactured with LPBF.