The quantification of three classes of graphite inclusions in cast iron, namely, nodular, flake, and irregular, is the most important process in the foundry industry. This classification is based on the ISO 945 proposed morphology of graphite inclusions. This work presents a novel solution for automatic quantitative analysis of graphite inclusions into the three mentioned classes. The proposed work comprises three stages, namely, preprocessing of micrographs, classification of graphite inclusions, and then quantification of inclusions in each class. An effort has been made in this work to propose a minimum set of features to represent graphite inclusion morphology. The method employs just two geometric shape descriptors: the diameter ratio and the area ratio. A fuzzy rule based classifier is built using known feature values that are efficient in the classification of the three classes of graphite inclusions. The proposed method is automatic, fast, and provides the basis for determining many more morphological parameters that can be determined with the least effort. The results obtained by the proposed method are compared with the manual method. It is observed that the results obtained from the proposed method are useful in the optimization of cast iron manufacturing in the foundry industry.

The digital image processing and computer vision technologies have key role to play in the field of material manufacturing and quality control. The microstructure images of materials provide vital information about materials properties. The microstructure visual and mechanical properties are strongly related. The mechanical properties, namely, yield strength, tensile strength and elongation, of ductile iron are directly dependent on ferrite composition and nodularity value of the material. Castings with poor nodularity will exhibit lower tensile elongation and often do not meet minimum tensile strength and finally impact strength requirements. Hence, it is established by experimental results that the composition and nodularity value of the material have paramount importance in material manufacturing.In this paper, a novel automatic method of digital image analysis for estimating important mechanical properties with the help of microstructure visual properties has been proposed. Microstructure image analysis is performed for deriving microstructure properties, namely, nodularity value and percentage of ferrite phase present in material sample. A fuzzy rule based inference system is built using known authentic relationship data published in the research literature [3] to estimate important mechanical properties of the sample material using nodularity value and percentage of ferrite phase. With the inputs, namely, percentage of ferrite phase and nodularity values, to fuzzy inference system, the mechanical properties, namely, yield strength, tensile strength and elongation are predicted. The nodularity of the samples were determined by using image analysis techniques based on ASTM A 247-67(1968) standard. The automatic image analysis minimized the variability of the measurement due to operator bias. The results of the proposed method are compared with results obtained by manual method. The results of proposed method are accurate and close to practical limits. The proposed method is easily repeatable, fast and economical and is expected to be useful in manufacturing of ductile cast iron and quality control practices.

Image fusion is an important process in the medical image diagnostics methods. Fusing images by obtaining information from different source and different types of images(modals) called multi-modal image fusion. This paper implements an effective and fast spatial domain based multimodal image fusion using moving frame based decomposition (MFDF)method. Images from two different modalities are taken and decomposed to texture and approximation components. Weight mapping strategy is applied along with the guide filtering to fuse the approximation components using the final map. Weight mapping using the guide filtering is used for the fusing the images from different modalities. MATLAB is used for algorithm implementation. The results obtained are comparatively competitive with the recent publication[11]. Multi modal image fusion thus implemented gives promising results, when compared to moving frame decomposition framework method. The size and the blurring variable of the guiding filter is optimized to obtain a better Structural Similarity Index Measurement (SSIM).

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Digital Image processing (DIP) and Computer vision (CV) techniques have great support role in material manufacturing by providing precise insight of materials. The morphology of constituents in metal alloys basically depends on the process of solidification. The solidification method (air, oil or water) and time are the reasons for definite morphology of constituents. Dendrite structures are one of the, such morphological structures and many important properties of materials are closely related to the morphology of the dendrite. The information about solidification process of materials is a must-know information in the process of production of materials which can be extracted through characterization of dendrite structures. In this paper, an automated and robust method that comprises of image processing, computer vision and serial sectioning techniques as a means of 3D characterization of the solidified microstructures of magnesium-based alloys is presented. The phase fraction and morphologies of intermetallics of magnesium -aluminium alloy material are determined. The results obtained by proposed method are compared with the manual computations based on the Scheil-Gulliver solidification model [12,13] for the authenticity of proposed method. The comparison of results indicates that the results of the proposed method are much accurate compared to other methods. Therefore, the proposed method will enable a comprehensive understanding of solidification variables, microstructure, and properties.