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

OBRABOTKAMETALLOV Vol. 23 No. 4 2021 TECHNOLOGY Application of digital image processing technique in the microstructure analysis and the machinability investigation Manojkumar Sheladiya 1, 2, a,* , Shailee Acharya 3, b , Ashish Kothari 2, c , Ghanshyam Acharya 4, d 1 Gujarat Technological University, Ahmedabad, 382424, India 2 Atmiya University, Faculty of Engineering & Technology, Yogidham Gurukul, Kalawad Road, Rajkot, 360005, India 3 Sardar Vallabhbhai Patel Institute of Technology, Af fi liated to GTU, Vasad, 388306, India 4 Atmiya Institute of Technology and Science, Yogidham Gurukul, Kalawad Road, Rajkot, 360005, India a https://orcid.org/0000-0002-9154-3355, mvsheladiya@gmail.com, b https://orcid.org/0000-0001-6428-8961, shailee.acharya@gmail.com , c https://orcid.org/0000-0002-1981-8465, amkothari.ec@gmail.com , d https://orcid.org/0000-0002-3580-3116 , gdacharya@rediffmail.com Obrabotka metallov (tekhnologiya, oborudovanie, instrumenty) = Metal Working and Material Science. 2021 vol. 23 no. 4 pp. 21–32 ISSN: 1994-6309 (print) / 2541-819X (online) DOI: 10.17212/1994-6309-2021-23.4-21-32 Obrabotka metallov - Metal Working and Material Science Journal homepage: http://journals.nstu.ru/obrabotka_metallov ARTICLE INFO Article history : Received: 11 July 2021 Revised: 30 July 2021 Accepted: 07 September 2021 Available online: 15 December 2021 Keywords : Machinability Index ASTMA 48 Class 20 K-means clustering Mould-metal interface Acknowledgment The group of authors is much obligated to the Krislur Castomech Pvt. Ltd., Bhavanagar, Gujarat, India for availing the facility for the experimentation. 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 fi rst 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 fl akes 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. For citation: SheladiyaM.V.,Acharya S.G., KothariA.M.,Acharya G.D.Application of digital image processing technique in the microstructure analysis and the machinability investigation. Obrabotka metallov (tekhnologiya, oborudovanie, instrumenty) = Metal Working and Material Science , 2021, vol. 23, no. 4, pp. 21–32. DOI: 10.17212/1994-6309-2021-23.4-21-32. (In Russian). ______ * Corresponding author Sheladiya Manojkumar V. , M.Tech.(Engineering), Assistant Professor Atmiya University, Faculty of Engineering & Technology, Yogidham Gurukul, Kalawad Road, 360005, Rajkot, Gujarat, India. Tel.: +91-9898278267, e-mail: mvsheladiya@gmail.com Introduction It is undoubted that the thermophysical properties of the material largely depend on the microstructure. Thus, its quantitative assessment and characterization become necessary for their prediction. Image analysis in this case may be of key importance. Modern image analysis software can accurately determine the number of structural elements in terms of size, shape and volume fraction [1]. When refers to the microstructure, it is usually meant the location of phases, defects and grain orientation. The phase has a certain chemical composition and/or crystal structure and is separated by a distinct boundary. Microstructures can be observed and analyzed using different microscopy techniques [2].