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The following work deals with the quantitative fracture surface evaluation in damage analysis. So far, fracture surfaces have almost exclusively been evaluated qualitatively, i.e. the presence of fracture features is documented and their surface proportions are estimated, if necessary. Many years of experience are required, as well as an intensive comparison with defined comparative images from the literature. The aim of this work is the development of classifiers which can recognize fracture mechanisms or fracture features in scanning electron microscope images (SEM). The basis is 46 SEM images, which have been evaluated by fractography experts with regard to fracture features. The existing data set of images is expanded using augmentation methods in order to increase the variability of the data and counteract overfitting. Only convolutional neural networks (CNN) are used to create the classifiers. Various network configurations are tested, with the SegNet achieving the best results.
Die „auffälligen großen Streifen“ sind von echten Schwingstreifen nur sehr schwer zu trennen, weil sie parallel zu den Schwingstreifen verlaufen und meistens ebenfalls nicht sehr groß sind. Die im Labor getesteten Proben zeigten, dass diese großen Streifen zusammen mit Verreibungen auftraten und „tire tracks“ („Reifenspuren“) verursachten. Dies wurde durch eine hochauflösende REM-Untersuchung bestätigt, die „tire tracks“ auf den „großen Streifen“ zeigte. Ein weiterer Hinweis ist die Änderung des R-Werts von -1 auf 0,1, was zu einer Bruchfläche ohne große Streifen und „tire tracks“ führt.
Due to the increasing scarcity of critical raw materials current high-temperature materials are sought to be replaced by alloys based on more abundant metals. One possibility within the class of intermetallics are iron aluminides, which combine sustainability and cost-efficiency with the prospect of mass savings. Iron aluminides show competitive specific strength up to 700 °C and excellent creep and wet corrosion resistance by small additions of Mo, Ti and B. Nevertheless, a Mo content of above 2 at.% which is needed for optimum corrosion resistance results in enhanced brittleness, especially at room temperature. This is why alloys with these Mo fractions were only mechanically tested under compressive loading so far. Still, testing of static and creep properties under tensile loading is required for reliable component design. Besides high standards for crack-free processing, data acquisition for tensile loads is especially complicated by environmental embrittling effects for iron aluminides. To cope with these challenges, the AiF research project “WAFEAL – Materials applications for iron aluminides” was initiated. The main goal is to collect standardised data on ambient and high-temperature tensile properties and creep properties. Samples with a nominal composition of Fe-26Al-4Mo-0.5Ti-1B [at.%] were manufactured via centrifugal casting in ceramic shell moulds followed by machining. Heat treatment for homogenisation and final polishing were carried out where appropriate. A summary of the achieved tensile and creep properties such as yield and tensile strength, maximum elongation, secondary creep rate and stress exponents will be given. Results will be also discussed regarding the influence of temperature, stress level and microstructure on the damage mechanisms. Furthermore, the effect of different alloy concentrations on the mechanical response at different temperatures will be outlined within a small experimental series.
The aim of a fractographic investigation is the evaluation of macroscopic and microscopic fracture surface characteristics and, as a result, the determination of the fracture mechanism of a component from a failure case. The basis for such evaluations of fracture characteristics comes from actual comparative mechanical testing and from the literature. A fractographic analysis can be very complex and, in any case, requires considerable experience. In the IGF project "iFrakto", software is being developed that quantitatively determines fracture characteristics and fracture mechanisms utilizing digitized expert knowledge, machine learning, and standard 2D and topographical data from SEM imaging. Topographical data are obtained from 4QBSE detector using shape-from-shading technology. In the medium term, a software tool should provide knowledge-based suggestions for the evaluation of fracture surfaces in real time during SEM work or at subsequent evaluation. As a basis for this, round robins were carried out among fractographers in order to create a knowledge base, to query the practice-relevant requirements for such tools and to carry out first practical tests. Actual results are presented and the relevance of the evaluation strategy is evaluated.
The aim of a fractographic investigation is the evaluation of macroscopic and microscopic fracture surface characteristics and, as a result, the determination of the fracture mechanism of a component from a failure case. The basis for such evaluations of fracture characteristics comes from actual comparative mechanical testing and from the literature. A fractographic analysis can be very complex and, in any case, requires considerable experience.
Machine learning methods enables the quantitative determination of fracture characteristics and fracture mechanisms utilizing digitized expert knowledge [1]. Although the application of SE images provides promising results, additional information is required to obtain reliable solutions. As expected, BSE and 3D information helps to improve the classification (Fig. 1). But only a fast, widely integrated, and automated topography measurement can provide the required amount of referenced surface data for the application of machine learning methods.
To fulfil these requirements, topographical data are obtained from a BSE detector with four symmetric segments (4Q-BSE) using shape-from-shading technology [2]. Surface height calculation is performed live during image acquisition and provides immediate feedback in three dimensions. All available signals (SE, BSE and more if applicable) are recorded simultaneously together with the surface topography and stored in a multichannel data file. This guaranties the same geometrical reference for all data, which is required for further analysis (Fig. 2).
When applying machine learning methods to topographic data together with SEM images, topographic information must be provided as depth image. Consequently, a unique height scale is required for all applied data with different magnifications. This requires a calibrated height measurement, which is ensured with the integrated 3D calibration of the topographic acquisition and a dedicated calibration sample. Thus, a large number of data sets from different fracture samples was generated and used as training data for machine learning.
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.
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.
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.