Advanced crack tip field characterization using conjugate work integrals

The quantitative characterisation of crack tip loads is fundamental in fracture mechanics. Although the potential influence of higher order terms on crack growth and stability is known, classical studies solely rely on first order stress intensity factors. We calculate higher order Williams coefficients using an integral technique based on conjugate work integrals and study the convergence with increasing crack tip distance. We compare the integral method to the state-of-the-art fitting method and provide results for higher-order terms with several crack lengths, external forces, and sizes for widely used middle tension, single-edge cracked tension, and compact tension specimen under mode-I loading

Physics-guided adversarial networks for artificial digital image correlation data generation

Digital image correlation (DIC) has become a valuable tool in the evaluation of mechanical experiments, particularly fatigue crack growth experiments. The evaluation requires accurate information of the crack path and crack tip position, which is difficult to obtain due to inherent noise and artefacts. Machine learning models have been extremely successful in recognizing this relevant information given labelled DIC displacement data. For the training of robust models, which generalize well, big data is needed. However, data is typically scarce in the field of material science and engineering because experiments are expensive and time-consuming. We present a method to generate synthetic DIC displacement data using generative adversarial networks with a physics-guided discriminator. To decide whether data samples are real or fake, this discriminator additionally receives the derived von Mises equivalent strain. We show that this physics-guided approach leads to improved results in terms of visual quality of samples, sliced Wasserstein distance, and geometry score

A Robot-Assisted Microscopy System for Digital Image Correlation in Fatigue Crack Growth Testing

Digital image correlation (DIC) with microscopes has become an important experimental tool in fracture mechanics to study local effects such as the plastic zone, crack closure, crack deflection or crack branching. High-resolution light microscopes provide 2D images but the field of view is limited to a small area and very sensitive to its alignment. A flexible positioning system is therefore needed to collect such DIC data during the entire fatigue crack growth process. Objective We present in our paper a new experimental setup for local high-resolution 2D DIC measurements at any location and at any time during fatigue crack growth experiments with a non-fixed DIC microscopy system. We use a robot to move the 2D DIC microscope to any location on the surface of the specimen. Optical and tactile methods automatically adjust the system and ensure highest image quality as well as accurate alignment. In addition, an advanced repositioning method reduces out-of-plane motion effects. The robot is able to achieve a repositioning accuracy of less than 0.06 mm in vector space, resulting in very low Von Mises strain scattering of 0.07 to 0.09% in the DIC evaluation. The system minimizes systematic errors caused by translation and rotational deviations. Effects such as crack deflection, crack branching or the plastic zone of a fatigue crack can be investigated with a field of view of 10.2 x 6.4 mm2. The robot supported DIC system generates up to 8000 high-quality DIC images in an experiment that enables the application of digital evaluation algorithms. Redundant information create confidence in the results as all revealed effects are comprehensible. This increases the information content of a single fatigue crack growth test and accelerates knowledge generation

Fatigue crack growth in anisotropic aluminium sheets -- phase-field modelling and experimental validation

Fatigue crack growth is decisive for the design of thin-walled structures such as fuselage shells of air planes. The cold rolling process, used to produce the aluminium sheets this structure is made of, leads to anisotropic mechanical properties. In this contribution, we simulate the fatigue crack growth with a phase-field model due to its superior ability to model arbitrary crack paths. A fatigue variable based on the Local Strain Approach describes the progressive weakening of the crack resistance. Anisotropy regarding the fracture toughness is included through a structural tensor in the crack surface density. The model is parameterised for an aluminium AA2024-T351 sheet material. Validation with a set of experiments shows that the fitted model can reproduce key characteristics of a growing fatigue crack, including crack path direction and growth rate, considering the rolling direction

Development and Test of a Low Emission Urban Delivery System

This paper presents the results of a Berlin research project in which a low emission urban delivery system was designed and tested in collaboration with different urban logistics stakeholders. First, the paper shows the developed concept that includes a macro-hub and corresponding micro-hubs, while the vehicle fleet consists of electric cargo bikes as well as an electric van. Second, the key results of the corresponding 6-month field trial are discussed. Parcels have been delivered to B2B recipients. To investigate the feasibility, the ecological and the economic impact of the developed concept, transport data was constantly collected during the field trial. Based on the data, average costs and emissions per parcel were calculated and compared to a conventional delivery system. Furthermore, managerial implications were derived. Finally, the limitations of the study and further research are summarized

Explainable machine learning for precise fatigue crack tip detection

Data-driven models based on deep learning have led to tremendous breakthroughs in classical computer vision tasks and have recently made their way into natural sciences. However, the absence of domain knowledge in their inherent design significantly hinders the understanding and acceptance of these models. Nevertheless, explainability is crucial to justify the use of deep learning tools in safety-relevant applications such as aircraft component design, service and inspection. In this work, we train convolutional neural networks for crack tip detection in fatigue crack growth experiments using full-field displacement data obtained by digital image correlation. For this, we introduce the novel architecture ParallelNets—a network which combines segmentation and regression of the crack tip coordinates—and compare it with a classical U-Net-based architecture. Aiming for explainability, we use the Grad-CAM interpretability method to visualize the neural attention of several models. Attention heatmaps show that ParallelNets is able to focus on physically relevant areas like the crack tip field, which explains its superior performance in terms of accuracy, robustness, and stability

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

Fatigue cracks are an inherent part in the lightweight design of engineering structures subjected to non-constant loads. Particularly important for airframe structures are accurate design data for crack initiation, stable fatigue crack propagation (FCP) and its rapid increase until ultimate failure. Non-straight crack paths are difficult or time-consuming to detect and monitor in laboratory experiments as well as in service using traditional techniques such as direct current potential drop (DCPD) or dye penetrant inspection. To this purpose, we implemented a deep convolutional neural network (CNN) to detect crack paths and especially their crack tips based on full-field displacement data obtained by 3D digital image correlation (DIC). Therefore, fatigue crack propagation experiments were performed for AA2024-T3 rolled sheet materials using 160 mm and 950 mm wide MT specimens. During the experiments, several hundred datasets were acquired by DIC and labelled by optical analysis. 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 cracks in all specimens based on the x and y displacements

Determination of Stress Intensity Factors and J integral based on Digital Image Correlation

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 KI and KII 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