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Automated, reliable, and objective microstructure inference from micrographs is essential for a comprehensive understanding of process-microstructure-property relations and tailored materials development. However, such inference, with the increasing complexity of microstructures, requires advanced segmentation methodologies. While deep learning offers new opportunities, an intuition about the required data quality/quantity and a methodological guideline for microstructure quantification is still missing. This, along with deep learning’s seemingly intransparent decision-making process, hampers its breakthrough in this field. We apply a multidisciplinary deep learning approach, devoting equal attention to specimen preparation and imaging, and train distinct U-Net architectures with 30–50 micrographs of different imaging modalities and electron backscatter diffraction-informed annotations. On the challenging task of lath-bainite segmentation in complex-phase steel, we achieve accuracies of 90% rivaling expert segmentations. Further, we discuss the impact of image context, pre-training with domain-extrinsic data, and data augmentation. Network visualization techniques demonstrate plausible model decisions based on grain boundary morphology. Segmentation and classification of microstructures are required by quality control and materials development. The authors apply deep learning for the segmentation of complex phase steel microstructures, providing a bridge between experimental and computational methods for materials analysis.

The inner structure of a material is called microstructure. It stores the genesis of a material and determines all its physical and chemical properties. While microstructural characterization is widely spread and well known, the microstructural classification is mostly done manually by human experts, which gives rise to uncertainties due to subjectivity. Since the microstructure could be a combination of different phases or constituents with complex substructures its automatic classification is very challenging and only a few prior studies exist. Prior works focused on designed and engineered features by experts and classified microstructures separately from the feature extraction step. Recently, Deep Learning methods have shown strong performance in vision applications by learning the features from data together with the classification step. In this work, we propose a Deep Learning method for microstructural classification in the examples of certain microstructural constituents of low carbon steel. This novel method employs pixel-wise segmentation via Fully Convolutional Neural Network (FCNN) accompanied by a max-voting scheme. Our system achieves 93.94% classification accuracy, drastically outperforming the state-of-the-art method of 48.89% accuracy. Beyond the strong performance of our method, this line of research offers a more robust and first of all objective way for the difficult task of steel quality appreciation.

Materials’ microstructures are signatures of their alloying composition and processing history. Automated, quantitative analyses of microstructural constituents were lately accomplished through deep learning approaches. However, their shortcomings are poor data efficiency and domain generalizability across data sets, inherently conflicting the expenses associated with annotating data through experts, and extensive materials diversity. To tackle both, we propose to apply a sub-class of transfer learning methods called unsupervised domain adaptation (UDA). UDA addresses the task of finding domain-invariant features when supplied with annotated source data and unannotated target data, such that performance on the latter is optimized. Exemplarily, this study is conducted on a lath-shaped bainite segmentation task in complex phase steel micrographs. Domains to bridge are selected to be different metallographic specimen preparations and distinct imaging modalities. We show that a state-of-the-art UDA approach substantially fosters the transfer between the investigated domains, underlining this technique’s potential to cope with materials variance.

The steel industry is undergoing a significant transformation driven by the urgent need to decarbonize the global economy. This transition aims to reduce CO₂ emissions substantially, impacting key sectors such as automotive, construction, transportation, and energy. The shift towards green steel production involves overcoming technological challenges, particularly the reliance on green electricity and hydrogen. This paper explores the technological pathways to achieving net-zero emissions in the steel sector, highlighting the potential for emissions reductions in other industries through the use of steel co-products. It discusses the primary and secondary steel production routes, the generation and utilization of by-products, and the integration of carbon capture and storage technologies. The paper also addresses the challenges of scrap availability and quality, the role of hydrogen in decarbonization, and the economic and regulatory factors influencing the industry's transition. Despite significant efforts, achieving net-zero emissions by 2050 remains uncertain, necessitating rapid implementation of low-carbon technologies and supportive policies to ensure the competitiveness of green steel production.

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.

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 microstructures, 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 parameters 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 foundation of materials science and engineering is the establishment of process–microstructure–property links, which in turn form the basis for materials and process development and optimization. At the heart of this is the characterization and quantification of the material’s microstructure. To date, microstructure quantification has traditionally involved a human deciding what to measure and included labor-intensive manual evaluation. Recent advancements in artificial intelligence (AI) and machine learning (ML) offer exciting new approaches to microstructural quantification, especially classification and semantic segmentation. This promises many benefits, most notably objective, reproducible, and automated analysis, but also quantification of complex microstructures that has not been possible with prior approaches. This review provides an overview of ML applications for microstructure analysis, using complex steel microstructures as examples. Special emphasis is placed on the quantity, quality, and variance of training data, as well as where the ground truth needed for ML comes from, which is usually not sufficiently discussed in the literature. In this context, correlative microscopy plays a key role, as it enables a comprehensive and scale-bridging characterization of complex microstructures, which is necessary to provide an objective and well-founded ground truth and ultimately to implement ML-based approaches.

Dual-phase steel shows a strong connection between its microstructure and its mechanical properties. This structure–property correlation is caused by the composition of the microstructure of a soft ferritic matrix with embedded hard martensite areas, leading to a simultaneous increase in strength and ductility. As a result, dual-phase steels are widely used especially for strength-relevant and energy-absorbing sheet metal structures. However, their use as heavy plate steel is also desirable. Therefore, a better understanding of the structure–property correlation is of great interest. Microstructure-based simulation is essential for a realistic simulation of the mechanical properties of dual-phase steel. This paper describes the entire process route of such a simulation, from the extraction of the microstructure by 3D tomography and the determination of the properties of the individual phases by nanoindentation, to the implementation of a simulation model and its validation by experiments. In addition to simulations based on real microstructures, simulations based on virtual microstructures are also of great importance. Thus, a model for the generation of virtual microstructures is presented, allowing for the same statistical properties as real microstructures. With the help of these structures and the aforementioned simulation model, it is then possible to predict the mechanical properties of a dual-phase steel, whose three-dimensional (3D) microstructure is not yet known with high accuracy. This will enable future investigations of new dual-phase steel microstructures within a virtual laboratory even before their production.

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.

Further development of high chromium cast irons (HCCI) is based on tailoring the microstructure, necessitating an accurate control over the phase transformation and carbide precipitation temperatures and can be achieved by thermal treatments (TT). To understand the underlying mechanisms controlling the transformation kinetics during the different stages of the TT, it is imperative to adjust the TT parameters to have information of the transformations occurring during non-thermal and isothermal heating cycles, since proper selection of the TT parameters ensures the optimum use of the alloying elements. In this work, the boundaries of the phase transformations for a HCCI containing 26 wt pct Cr for different cooling rates (continuous cooling transformation, CCT, diagram) were established by applying dilatometric measurements. Based on the CCT diagram, a temperature-time-transformation (TTT) diagram was constructed by isothermally holding the samples until complete phase transformation. For determining the initiation and finishing of the transformation, the lever rule assisted by derivatives was applied. The phases present after transformation were determined by combining X-ray diffraction (XRD) and metallographic characterization using optical microscopy (OM) and scanning electron microscopy (SEM). Finally, the data obtained from the dilatometer was experimentally verified by isothermally heat treating some samples using laboratory furnaces. The transformed phase fraction from OM and SEM images was then correlated to the fraction obtained from the TTT diagram.