Application of digital image processing technique in the microstructure analysis and the machinability investigation

Abstract

Introduction. The world is at the stage of creating an interdisciplinary approach that will be implemented in metallurgical research. The paper formulates the technique of image analysis in the study of processing at different depths from the mold-metal interface. The purpose of the work. Processing of a cast-iron workpiece within the first 3.5 mm of thickness from the mold-metal interface is a serious problem of solid processing. The study of machinability at different depths is a key requirement of the industry for ease of processing. Machinability will determine a number of factors, including tool consumption, workpiece surface quality, energy consumption, etc. The method of investigation. Image analysis is performed to determine the percentage of graphite in etched and non-etched samples. K-means clustering allows to create a new image from a given one with a clear separation of white and black areas by converting a digital image into a binary image using a threshold value for segmentation. The volume fraction of perlite, the volume fraction of graphite and the average size of graphite flakes in microns are used as input variables for the machinability of cast iron. Results and discussion. The output, that is, the segmented image, will be the input function for calculating the workability index using formulas. Thus, microstructural analysis will help predict the workability index of grey cast iron ASTM A48 Class 20. Using this method and the program, based on the microstructure, it is possible to predict in advance the characteristics of the machining of the part, taking into account possible changes in the casting process itself.