Surface Morphology Identification of Steel Natural Ferrite-Martensitic Composite Using ImageJ Software

Introduction. Modern materials require a deep understanding of their structure in order to predict their performance properties. However, the use of various imaging techniques and programs, such as optical and electron microscopy, is limited to two-dimensional images, making it difficult to fully analyze the morphology of materials. Despite research in this field, there is still a lack of knowledge about the three-dimensional organization of materials, leading to gaps in our understanding of how geometry affects the physical properties of composite materials. ImageJ was chosen for this study due to its versatility and ability to support multiple formats, simplifying the process of analysis. It also offers powerful tools for automated processing and allows users to extract three-dimensional information from two-dimensional images. This is crucial for accurately identifying structural components. The current study aims to fill in the missing information by analyzing the morphology of a steel ferrite-martensite composite. The aim of the work is to determine the 3D surface structure of the composite, which will improve understanding of its performance characteristics and confirm the significance of selecting appropriate visualization techniques.

Materials and Methods. An image of the microstructure of a steel natural ferrite-martensitic composite (NFMC), obtained using a Metam PB–22 optical microscope, was chosen as the starting material for analysis. The microstructure in question consists of two phases: the light phase being ferrite and the dark phase being martensite. The ImageJ program, which has been adapted to various formats of electron microscopic and metallographic images, was used to obtain a wide range of geometric characteristics of the surface.

Results. A study using ImageJ software on the microstructure of a steel ferrite-martensitic composite revealed a characteristic lineage structure consisting of a light phase (ferrite) and a dark phase (martensite). Image processing, including scaling and segmentation, led to the conversion to black and white format, allowing for clear visualization of the boundaries between the phases and the geometric shapes of the particles. The four-parameter Rodbard calibration function provided additional data on area, standard deviation, skewness, and kurtosis, making it difficult to analyze the structure. As a result, ferrite occupied 40.8% of the area, while martensite occupied 59.2%. The surface profile revealed an alternating pattern of misoriented crystals, and the quantitative information allowed for the creation of a clear 3D image of the composite surface.

Discussion. The thickness of grain boundaries in pixels was found to be thinner in this graphic editor than in others, which affected the area and, consequently, the amount of light phase. The change in the quantitative ratio of ferrite-martensite phases was due to the program's ability to suppress image “noise” and more clearly read the unrecognized gray phase, with some of it belonging to the light phase and some to the dark phase.

With the advancement of technology and the increasing demands for strength and wear resistance, understanding the microstructure of materials has become crucial for optimizing their properties. The selection of appropriate imaging techniques, such as the use of ImageJ software, not only allows for accurate data on phase distribution, but also contributes to a more in-depth analysis of mechanical properties such as hardness and corrosion resistance. These aspects are important in the context of the development of innovative technologies where reliability and durability are essential factors.

Conclusion. The use of the ImageJ software package for visualization in 2D and 3D graphics and qualitative and quantitative analysis of the surface morphology of heterogeneous structural states of materials is a convenient, effective and informative way to obtain geometric characteristics of particles of structural components. It is also possible to map the shape and size of particles. Automation of this process leads to time and resource savings, minimizing the influence of subjective factors on results at different stages of analysis. Identification of the 3D surface structure of composites helps to deepen our understanding of their operational characteristics, which is crucial in the context of modern technological demands. This knowledge allows us to develop new materials with improved properties such as strength, wear and corrosion resistance. Furthermore, it enables us to predict how materials will perform in actual conditions.

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