Stahlgefüge besser verstehen – Kontrastierung, Bildanalyse und Klassifizierung niedriglegierter Stähle

Ziel der vorliegenden Arbeit ist es, aufbauend auf dem EU-Projekt Micro-Quant, eine Methode zu entwickeln, um die komplexen Gefüge thermo-mechanisch gewalzter, niedriglegierter Stähle quantitativ beschreiben und klassifizieren zu können. Klassische Ansätze der Metallographie, Bildverarbeitung und Gefügeanalyse stoßen bei der Quantifizierung und Klassifizierung derartiger Stähle zunehmend an ihre Grenzen. Durch die gezielte (in situ) Untersuchung und Anwendung von Farbätzungen nach LePera und Beraha konnten thermo-mechanisch gewalzte, niedriglegierte Stähle optimal und reproduzierbar – sowohl für die Analyse mittels Lichtmikroskopie als auch mittels Rasterelektronenmikroskopie – kontrastiert werden. Zur vollständigen Gefügebeschreibung wurden darüber hinaus Arbeitsabläufe zur Bildregistrierung und Merkmalsextraktion in Fiji und Matlab implementiert sowie neue Segmentierungsansätze nach Chan & Vese erfolgreich etabliert. Mit dem in der vorliegenden Arbeit entwickelten, ganzheitlichen Ansatz, der von der Probenpräparation über die Kontrastierung hin zur Segmentierung reicht, konnte schließlich eine objektive und automatische Gefügeklassifizierung mit den Methoden des maschinellen Lernens (Support Vector Machine und Deep Learning) entwickelt werden. Für den gegebenen Probensatz konnten somit Klassifizierungsgenauigkeiten von bis zu 95% erreicht werden. Die entwickelte Methodik ist nicht nur auf andere Stähle anwendbar, sondern kann als Vorlage für sämtliche Materialklassen herangezogen werden

Microstructural Classification of Bainitic Subclasses in Low-Carbon Multi-Phase Steels Using Machine Learning Techniques

With its excellent property combinations and ability to specifically adjust tailor-made microstructures, steel is still the world’s most important engineering and construction material. To fulfill ever-increasing demands and tighter tolerances in today’s steel industry, steel research remains indispensable. The continuous material development leads to more and more complex microstruc tures, which is especially true for steel designs that include bainitic structures. This poses new challenges for the classification and quantification of these microstructures. Machine learning (ML) based microstructure classification offers exciting potentials in this context. This paper is concerned with the automated, objective, and reproducible classification of the carbon-rich second phase objects in multi-phase steels by using machine learning techniques. For successful applications of ML-based classifications, a holistic approach combining computer science expertise and material science domain knowledge is necessary. Seven microstructure classes are considered: pearlite, martensite, and the bainitic subclasses degenerate pearlite, debris of cementite, incomplete transformation product, and upper and lower bainite, which can all be present simultaneously in one micrograph. Based on SEM images, textural features (Haralick parameters and local binary pattern) and morphological parame ters are calculated and classified with a support vector machine. Of all second phase objects, 82.9% are classified correctly. Regarding the total area of these objects, 89.2% are classified correctly. The reported classification can be the basis for an improved, sophisticated microstructure quantification, enabling process–microstructure–property correlations to be established and thereby forming the backbone of further, microstructure-centered material development

The Effect of Thermal Processing and Chemical Composition on Secondary Carbide Precipitation and Hardness in High-Chromium Cast Irons

The excellent abrasion resistance of high-chromium cast irons (HCCIs) is given by an optimal combination of hard eutectic and secondary carbides (SC) and a supporting matrix. The tailoring of the microstructure is performed by heat treatments (HTs), with the aim to adjust the final properties (such as hardness and abrasion resistance). In this work, the influence of chemical composition on the microstructure and hardness of HCCI_26%Cr is evaluated. An increase in the matrix hardness was detected after HTs resulting from combining precipitation of M23C6 SC during destabilization, and austenite/martensite transformation during quenching. Kinetic calculations of the destabilization process showed that M7C3 secondary carbides are the first to precipitate during heating, reaching a maximum at 850 °C. During subsequent heating up to 980 °C and holding at this temperature, they transformed completely to M23C6. According to the MatCalc simulations, further precipitation of M23C6 occurred during cooling, in the temperature range 980–750 °C. Both phenomena were related to experimental observations in samples quenched after 0-, 30-, 60- and 90-min destabilization, where M23C6 SC were detected together with very fine SC precipitated in areas close to eutectic carbides

Segmentation of Lath-Like Structures via Localized Identification of Directionality in a Complex-Phase Steel

In this work, a segmentation approach based on analyzing local orientations and directions in an image, in order to distinguish lath-like from granular structures, is presented. It is based on common image processing operations. A window of appropriate size slides over the image, and the gradient direction and its magnitude inside this window are determined for each pixel. The histogram of all possible directions yields the main direction and its directionality. These two parameters enable the extraction of window positions which represent lath-like structures, and procedures to join these positions are developed. The usability of this approach is demonstrated by distinguishing lath-like bainite from granular bainite in so-called complex-phase steels, a segmentation task for which automated procedures are not yet reported. The segmentation results are in accordance with the regions recognized by human experts. The approach’s main advantages are its use on small sets of images, the easy access to the segmentation process and therefore a targeted adjustment of parameters to achieve the best possible segmentation result. Thus, it is distinct from segmentation using deep learning which is becoming more and more popular and is a promising solution for complex segmentation tasks, but requires large image sets for training and is difficult to interpret

Quantitative analysis of mixed niobium-titanium carbonitride solubility in HSLA steels based on atom probe tomography and electrical resistivity measurements

Solubility of mixed niobium-titanium carbonitrides in commercially relevant High Strength Low-Alloy (HSLA) steel was investigated by combined use of electrical re sistivity measurements and APT after interrupted quenching from soaking temperatures between 950 and 1250 C. Increasing electrical resistivity of the bulk material towards higher soaking temperatures was proportional to the nominal niobium addition which was varied between 0.002 and 0.022e0.043e0.085 wt.-%. Correlative APT analysis of the solutes in the steel matrix showed good agreement with electrical resistivity. Investigating numerous precipitate particles, APT also derived a precise composition for mixed niobium titanium-carbonitrides which constitute the steel microstructure after casting/before soaking. The scavenging of microalloy elements by insoluble titanium nitrides was cor rected by means of combined APT analysis of such precipitate and a quantitative image analysis for the estimation of the total volume fraction of titanium nitrides. For the first time, solute and precipitate composition together were used for solubility calculations of such mixed carbonitrides to derive an experimental solubility product. This was compared to solubility products of well-established simple carbides and nitrides and theoretical calculations of the solubility of multicomponent carbonitrides. The large discrepancy between experimentally derived and modelled solubility emphasizes the ne cessity of a robust methodology for the quantification of microalloy precipitation in HSLA steels

Classification of Bainitic Structures Using Textural Parameters and Machine Learning Techniques

Bainite is an essential constituent of modern high strength steels. In addition to the still great challenge of characterization, the classification of bainite poses difficulties. Challenges when dealing with bainite are the variety and amount of involved phases, the fineness and complexity of the structures and that there is often no consensus among human experts in labeling and classifying those. Therefore, an objective and reproducible characterization and classification is crucial. To achieve this, it is necessary to analyze the substructure of bainite using scanning electron microscope (SEM). This work will present how textural parameters (Haralick features and local binary pattern) calculated from SEM images, taken from specifically produced benchmark samples with defined structures, can be used to distinguish different bainitic microstructures by using machine learning techniques (support vector machine). For the classification task of distinguishing pearlite, granular, degenerate upper, upper and lower bainite as well as martensite a classification accuracy of 91.80% was achieved, by combining Haralick features and local binary pattern

Reproducible Quantification of the Microstructure of Complex Quenched and Quenched and Tempered Steels Using Modern Methods of Machine Learning

Current conventional methods of evaluating microstructures are characterized by a high degree of subjectivity and a lack of reproducibility. Modern machine learning (ML) approaches have already shown great potential in overcoming these challenges. Once trained with representative data in combination with objective ground truth, the ML model is able to perform a task properly in a reproducible and automated manner. However, in highly complex use cases, it is often not possible to create a definite ground truth. This study addresses this problem using the underlying showcase of microstructures of highly complex quenched and quenched and tempered (Q/QT) steels. A patch-wise classification approach combined with a sliding window technique provides a solution for segmenting entire microphotographs where pixel-wise segmentation is not applicable since it is hardly feasible to create reproducible training masks. Using correlative microscopy, consisting of light optical microscope (LOM) and scanning electron microscope (SEM) micrographs, as well as corresponding data from electron backscatter diffraction (EBSD), a training dataset of reference states that covers a wide range of microstructures was acquired in order to train accurate and robust ML models in order to classify LOM or SEM images. Despite the enormous complexity associated with the steels treated here, classification accuracies of 88.8% in the case of LOM images and 93.7% for high-resolution SEM images were achieved. These high accuracies are close to super-human performance, especially in consideration of the reproducibility of the automated ML approaches compared to conventional methods based on subjective evaluations through experts

Tracing Microalloy Precipitation in Nb-Ti HSLA Steel during Austenite Conditioning

The microalloying with niobium (Nb) and titanium (Ti) is standardly applied in low carbon steel high-strength low-alloy (HSLA) steels and enables austenite conditioning during thermo-mechanical controlled processing (TMCP), which results in pronounced grain refinement in the finished steel. In that respect, it is important to better understand the precipitation kinetics as well as the precipitation sequence in a typical Nb-Ti-microalloyed steel. Various characterization methods were utilized in this study for tracing microalloy precipitation after simulating different austenite TMCP conditions in a Gleeble thermo-mechanical simulator. Atom probe tomography (APT), scanning transmission electron microscopy in a focused ion beam equipped scanning electron microscope (STEM-on-FIB), and electrical resistivity measurements provided complementary information on the precipitation status and were correlated with each other. It was demonstrated that accurate electrical resistivity measurements of the bulk steel could monitor the general consumption of solute microalloys (Nb) during hot working and were further complemented by APT measurements of the steel matrix. Precipitates that had formed during cooling or isothermal holding could be distinguished from strain-induced precipitates by corroborating STEM measurements with APT results, because APT specifically allowed obtaining detailed information about the chemical composition of precipitates as well as the elemental distribution. The current paper highlights the complementarity of these methods and shows first results within the framework of a larger study on strain-induced precipitation

Secondary carbides in high chromium cast irons: An alternative approach to their morphological and spatial distribution characterization

Secondary carbides precipitated in high chromium cast irons during thermal treatments were characterized by means of different characterization techniques, including scanning electron microscopy, energy dispersive X-ray spectroscopy, electron backscattered diffraction and a combination of chemical etching with confocal scanning laser microscopy. This set of techniques provides a full morphological, chemical and crystallographic description of the analysed phases. This work evaluated different methods for optimizing the image acquisition for a further image analysis (IA) based on the threshold binarization. Finally, the carbide size, distribution and morphology were determined after IA of the images acquired by aforementioned characterization techniques. Although the different techniques report some dispersion in the value for the average particle size, the particle inter-spacing and aspect ratio meet within the error value. The proposed characterization methodology provides statistically reliable data for a further evaluation of related physical properties in composites