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The damage process of short glass fibre (30% weight) reinforced polyamide caused by mechanical loading was investigated from the beginning on micro cracking level to the incipient crack of mm-dimension. Based on high resolution computer tomography and the X-ray-refraction technique the inner surface due to micro-cracking at the short fibre ends and the fibre matrix debonding of the skin surface of the filament was determined quantitatively. With the knowledge of the fatigue crack propagation rate and fracture toughness of the material from former research projects, it was derived that the total inner surface due to micro cracks measured by X-ray refraction is much higher than the specimen could have withstand the load, supposed the surface is in a localized crack. Hence, the damage process could be described from micro to macro level. Accompanying fractographic investigations endorse the modelling based on the NDT-techniques.
The damage process of short glass fibre (30% weight) reinforced polyamide caused by mechanical loading was investigated from the beginning on micro cracking level to the incipient crack of mm-dimension. Based on high resolution computer tomography and the X-ray-refraction technique the inner surface due to micro-cracking at the short fibre ends and the fibre matrix debonding of the skin surface of the filament was determined quantitatively. With the knowledge of the fatigue crack propagation rate and fracture toughness of the material from former research projects, it was derived that the total inner surface due to micro cracks measured by X-ray refraction is much higher than the specimen could have withstand the load, supposed the surface is in a localized crack. Hence, the damage process could be described from micro to macro level. Accompanying fractographic investigations endorse the modelling based on the NDT-techniques.
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 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.
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.
Effect of weld penetration depth on hydrogen-assisted cracking of high-strength structural steels
(2019)
The need for steels with highest mechanical properties is a result of the increasing demands for energy and resource efficiency. In this context, high-strength structural (HSS) steels are used in machine, steel and crane construction with yield strength up to 960 MPa. However, welding of HSS steels requires profound knowledge of three factors in terms of avoidance of hydrogen-assisted cracking (HAC): the interaction of (1) microstructure, (2) local stress/strain and (3) local hydrogen concentration. In addition to the three main factors, the used weld-arc process is also important for the performance of the welded joint, especially when using modern arc variants. In the past, the conventional transitional arc process (Conv. A) was mainly used for welding of HSS grades. In the past decade, the so-called modified spray arc process (Mod. SA) has been used increasingly for welding production. This modified process enables reduced seam opening angles with increased deposition rates compared to the conventional process. Economic benefits of using this arc type are: a reduced number of necessary weld beads and a lower weld seam volume, which result in decreased total welding time and costs. Nonetheless, investigations on a high-strength S960QL showed significantly higher hydrogen concentrations in the weld metal at a reduced seam opening angle with Mod. SA. This indicates an increased susceptibility of the welded component to HAC. Hence, existing recommendations on HAC-avoidance cannot be transferred directly to the Mod. SA-process. In the present study, the susceptibility to HAC of the HSS steel S960QL with same type of filler material was investigated. For that purpose, both Conv. A and Mod. SA were used with same heat input at different deposition rates. For assessment of the HAC susceptibility, the externally loaded implant test was used. Both conducted test series with Conv. A and Mod. SA showed similar crack critical stress of about 280 MPa. Below this value, no delayed fracture appeared. The welds with Mod. SA showed higher hydrogen concentrations. The fracture occurred in the heat-affected zone (HAZ) or in the weld metal (WM). However, the test series with Mod. SA showed a significant extension of the time to failure of several hours compared to tests carried out with Conv. A.
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.
Application of 4Q-BSE detector and dedicated software enables 3D surface measurements of fracture samples in SEM. 3D surface measurement is independent from magnification and material invariant. Quantitative height measurements require careful detector adjustment and system calibration. Live 3D view allows a fast assessment of topographical features. In addition to SE and BSE imaging, 3D surface data help to analyse and interpret fracture samples.
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.