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.
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.
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