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Fractography plays a critical role in failure analysis of engineering components and has a considerable importance for safety investigations. Usually, the interpretation of fracture surfaces is done by experts with the help of literature and experimental data, that requires a lot of experience. The use of deep learning (DL) with neural networks in failure analysis becomes more and more relevant with the rapidly developing possibilities. Especially, the modern network architectures can assist fractographers in determining various fracture features on SEM images of the fracture surfaces. The basis for the best possible evaluation is the understanding of the influence of the input data used for training deep neural networks (DNN). Therefore, this study discusses the influence of the selection of the input data used for the prediction quality of these networks in order to take this into account for future data acquisition. Specimens of various metallic materials were subjected to fatigue cracking experiment under laboratory conditions. The fractured surfaces were then imaged using various modes or detectors (such as SE, BSE and topography) in SEM, and those captured images were used to create a training data set. The relevance of the individual data for the quality of the prediction is determined by a specific combination of the different detector data. For the training, the well-established architecture of a UNet-ResNet34 with a fixed set of hyperparameters is used. It has been found in this present study that the combination of all input data significantly increases the prediction accuracy, whereby even the combination of SE and BSE data provides considerable advantages over the exclusive use of SE images.
In failure analysis, micro-fractographic analysis of fracture surfaces is usually performed based on practical knowledge which is gained from available studies, own comparative tests, from the literature, as well as online databases. Based on comparisons with already existing images, fracture mechanisms are determined qualitatively. These images are mostly two-dimensional and obtained by light optical and scanning electron imaging techniques. So far, quantitative assessments have been limited to macrocopically determined percentages of fracture types or to the manual measurement of fatigue striations, for example. Recently, more and more approaches relying on computer algorithms have been taken, with algorithms capable of finding and classifying differently structured fracture characteristics. For the Industrial Collective Research (Industrielle Gemeinschaftsforschung, IGF) project “iFrakto” presented in this paper, electron-optical images are obtained, from which topographic information is calculated. This topographic information is analyzed together with the conventional 2D images. Analytical algorithms and deep learning are used to analyze and evaluate fracture characteristics and are linked to information from a fractography database. The most important aim is to provide software aiding in the application of fractography for failure analysis. This paper will present some first results of the project.
High-strength structural steels are used in machine, steel, and crane construction with yield strength up to 960 MPa. However, welding of these steels requires profound knowledge of three factors in terms of avoidance of hydrogen-assisted cracking (HAC): the interaction of microstructure, local stress/strain, and local hydrogen concentration. In addition to the three main factors, the used arc process is also important for the performance of the welded joint. In the past, the conventional transitional arc process (Conv. A) was mainly used for welding of high-strength steel grades. In the past decade, the so-called modified spray arc process (Mod. SA) has been increasingly used for welding production. This modified process enables reduced seam opening angles with increased deposition rates compared with the Conv. A. Economic benefits of using this arc type are a reduction of necessary weld beads and required filler material. In the present study, the susceptibility to HAC in the heat-affected zone (HAZ) of the high-strength structural steel S960QL was investigated with the externally loaded implant test. For that purpose, both Conv. A and Mod. SA were used with same heat input at different deposition rates. Both conducted test series showed same embrittlement index “EI” of 0.21 at diffusible hydrogen concentrations of 1.3 to 1.6 ml/100 g of arc weld metal. The fracture occurred in the HAZ or in the weld metal (WM). However, the test series withMod. SA showed a significant extension of the time to failure of several hours compared with tests carried out with Conv. A.