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To this date the safety assessment of materials, used for example in the nuclear power sector, commonly relies on a fracture mechanical analysis utilizing macroscopic concepts, where a global load quantity K or J is compared to the materials fracture toughness curve. Part of the experimental effort involved in these concepts is dedicated to the quantitative analysis of fracture surfaces. Within the scope of this study a methodology for the semi-supervised training of deep learning models for fracture surface segmentation on a macroscopic level was established. Therefore, three distinct and unique datasets were created to analyze the influence of structural similarity on the segmentation capability. The structural similarity differs due to the assessed materials and specimen, as well as imaging-induced variance due to fluctuations in image acquisition in different laboratories. The datasets correspond to typical isolated laboratory conditions, complex real-world circumstances, and a curated subset of the two. We implemented a weak-to-strong consistency regularization for semi-supervised learning. On the heterogeneous dataset we were able to train robust and well-generalizing models that learned feature representations from images across different domains without observing a significant drop in prediction quality. Furthermore, our approach reduced the number of labeled images required for training by a factor of 6. To demonstrate the success of our method and the benefit of our approach for the fracture mechanics assessment, we utilized the models for initial crack size measurements with the area average method. For the laboratory setting, the deep learning assisted measurements proved to have the same quality as manual measurements. For models trained on the heterogeneous dataset, very good measurement accuracies with mean deviations smaller than 1 % could be achieved..., Comment: During review title changed to: Deep learning based initial crack size measurements utilizing macroscale fracture surface segmentation

As recent trends in manufacturing engineering disciplines show a clear development in the sustainable as well as economically efficient design of forming processes, monitoring techniques have been gaining in relevance. In terms of monitoring of product properties, most processes are currently open-loop controlled, entailing that the microstructure evolution, which determines the final product properties, is not considered. However, a closed-loop control that can adjust and manipulate the process actuators according to the required product properties of the component will lead to a considerable increase in efficiency of the processes regarding resources and will decrease postproduction of the component. For most forming processes, one set of component dimensions will result in a certain set of product properties. However, to successfully establish closed-loop property controls for the processes, a systematic understanding of the reciprocity of the dimensions after forming and final product properties must be established. This work investigates the evolution of dimensional accuracy as well as product properties for a series of forming processes that utilize different degrees of freedom for process control.

Crack initiation governs high cycle fatigue life and is susceptible to microstructural details. While corresponding microstructure-sensitive models are available, their validation is difficult. We propose a validation framework where a fatigue test is mimicked in a sub-modeling simulation by embedding the measured microstructure into the specimen geometry and adopting the experimental boundary conditions. Exemplary, a phenomenological crystal plasticity model was applied to predict deformation in ferritic steel (EN1.4003). Hotspots in commonly used fatigue indicator parameter maps are compared with damage segmented from micrographs. Along with the data, the framework is published for benchmarking future micromechanical fatigue models.

In order to improve the understanding of damage evolution in mechanically heterogeneous microstructures, like the ones of dual-phase steels, the influence of the applied stress state is a key element. In this work, we studied the influence of the globally applied stress state on the evolution of damage in such a microstructure. Classical damage models allow predictions of damage during deformation based on considerations of the material as an isotropic continuum. Here, we investigate their validity in a dual phase microstructure that is locally dominated by its microstructural morphological complexity based on a statistical ensemble of thousands of individual voids formed under different stress states. For this purpose, we combined a calibrated material model incorporating damage formation to assess the local stress state in samples with different notch geometries and high-resolution electron microscopy of large areas using a deep learning-based automated micrograph analysis to detect and classify microstructural voids according to their source of origin. This allowed us to obtain both the continuum stress state during deformation and statistically relevant data of individual void formation. We found that the applied plastic strain is the major influence on the overall number, and therefore the nucleation of new voids, while triaxiality correlates with the median void size, supporting its proposed influence on void growth. In contrast, coalescence of voids leading to failure appears related to local instabilities in the form of shear band formation and is therefore only indirectly determined by the global stress state in that it determines the global distribution, density and size of voids., Comment: 20 pages, 11 figures

Many studies have examined the damage behaviour of dual-phase steels already. It is a topic of high interest, since understanding the mechanisms of damage during forming processes enables the production of steels with improved properties and damage tolerance. However, the focus was rarely on the comparison between representatives of this steel class, and the numerical simulation for the quantification of damage states was not thoroughly used. Therefore, this study compares the damage initiation and accumulation of two dual-phase steels (DP800 and DP1000), which are used in the automotive industry. Additionally, parameter sets of a phenomenological damage mechanics model with coupled damage evolution are calibrated for each material. The combined analysis reveals an earlier initiation of damage for the DP800, where the damage accumulation phase is prolonged. For DP1000 the damage nucleates only shortly before material failure. The material model is able to correctly predict the behaviour, while experimental analysis confirms the prediction via light optical and SEM metallography.