5 Werkstofftechnik
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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.
The increasing importance of resource availability and closed-loop material cycles are driving materials research to reduce alloying content in conventional materials or even substitute them with more sustainable alternatives. Intermetallic iron aluminide alloys (FeAl) present a potential alternative. Many alloy concepts for improved high-temperature properties or ductility have already been successfully implemented in casting technologies on a laboratory scale. However, successful testing of FeAl alloys on an industrial scale was still pending at the beginning of the project.
Therefore, the aim of the project was to develop simulation based casting concepts for industrial casting processes using the base alloy Fe-26Al-4Mo-0.5Ti-1B and to narrow down process limits by means of hot cracking tests. Findings were transferred into practice-oriented guidelines for casting of iron aluminides, which is accessible to future applicants in SMEs. The focus was placed on centrifugal casting combined with investment casting or die casting. In addition to numerous design and casting process parameters, heat treatments and alloying additions (Al, Mo, B) were varied to determine the influence of alloying elements on castability, microstructure and mechanical properties. Data from microstructure analyses (microscopic imaging, determination of grain sizes as well as phase compositions and volume fractions, fractography), mechanical tests (hardness measurements, compression tests, ambient and high-temperature tensile tests, creep tests) as well as measurements of thermophysical properties could be generated on the base alloy. Correlations of materials data with process variables allowed conclusions to be drawn on strengthening mechanisms and ductility of the alloy and how they can be controlled in terms of processing and component design. Successful casting of highly complex components with thin wall thicknesses and optimised alloy compositions points out prospects for new fields of application.
The increasing importance of resource availability and closed-loop material cycles are driving materials research to reduce alloying content in conventional materials or even substitute them with more sustainable alternatives. Intermetallic iron aluminide alloys (FeAl) present a potential alternative. Many alloy concepts for improved high-temperature properties or ductility have already been successfully implemented in casting technologies on a laboratory scale. However, successful testing of FeAl alloys on an industrial scale was still pending at the beginning of the project.
Therefore, the aim of the project was to develop simulation-based casting concepts for industrial casting processes using the base alloy Fe-26Al-4Mo-0.5Ti-1B and to narrow down process limits by means of hot cracking tests. Findings were transferred into practice-oriented guidelines for casting of iron aluminides, which is accessible to future applicants in SMEs. The focus was placed on centrifugal casting combined with investment casting or die casting. In addition to numerous design and casting process parameters, heat treatments and alloying additions (Al, Mo, B) were varied to determine the influence of alloying elements on castability, microstructure and mechanical properties. Data from microstructure analyses (microscopic imaging, determination of grain sizes as well as phase compositions and volume fractions, fractography), mechanical tests (hardness measurements, compression tests, ambient and high-temperature tensile tests, creep tests) as well as measurements of thermophysical properties could be generated on the base alloy. Correlations of materials data with process variables allowed conclusions to be drawn on strengthening mechanisms and ductility of the alloy and how they can be controlled in terms of processing and component design. Successful casting of highly complex components with thin wall thicknesses and optimised alloy compositions points out prospects for new fields of application.