Dimensional analysis and ANN simulation of chip-tool interface temperature during turning SS304

OBRABOTKAMETALLOV Vol. 23 No. 3 2021 MATERIAL SCIENCE EQUIPMENT. INSTRUMENTS 4 Dimensional analysis and ANN simulation of chip-tool interface temperature during turning SS304 Atul Kulkarni 1, a , Satish Chinchanikar 1, b, * , Vikas Sargade 2, c 1 Vishwakarma Institute of Information Technology, Survey No. 3/4, Kondhwa (Budruk), Pune - 411048, Maharashtra, India 2 Dr. Babasaheb Ambedkar Technological University, Vidyavihar, Lonere, Dist. Raigad - 402103, Maharashtra, India a https://orcid.org/0000-0002-6452-6349, atul.kulkarni@viit.ac.in , b https://orcid.org/0000-0002-4175-3098, satish.chinchanikar@viit.ac.in , c https://orcid.org/0000-0001-8855-112X, vgsargade@dbatu.ac.in Obrabotka metallov (tekhnologiya, oborudovanie, instrumenty) = Metal Working and Material Science. 2021 vol. 23 no. 4 pp. 47–64 ISSN: 1994-6309 (print) / 2541-819X (online) DOI: 10.17212/1994-6309-2021-23.4-47-64 Obrabotka metallov - Metal Working and Material Science Journal homepage: http://journals.nstu.ru/obrabotka_metallov ARTICLE INFO Article history : Received: 29 July 2021 Revised: 19 August 2021 Accepted: 07 September 2021 Available online: 15 December 2021 Keywords : Chip-tool interface temperature Dimensional analysis Arti fi cial neural network Coated tools SS304 ABSTRACT Introduction. During machining, the resulting temperature has a wider and more critical impact on machining performance. During machining, the power consumption is mainly converted into heat near the cutting edge of the tool. Almost all the work performed during plastic deformation turns into heat. Researchers have put a lot of effort into measuring the cutting temperature during machining, as it signi fi cantly affects tool life and overall machining performance. The purpose of the work: to investigate the temperature of the chip-tool interface, taking into account the in fl uence of cutting parameters and the type of tool coating during SS304 turning. The chip-tool interface temperature is measured by changing the cutting speed and feed with a constant cutting depth for uncoated and PVD single-layer TiAlN and multi-layer TiN / TiAlN coated carbide tools. In addition, an attempt is made to develop a model for predicting the temperature of the chip-tool interface using dimensional analysis and ANN simulating to better understand the process. The methods of investigation. Experiments are carried out with varying the cutting speed (140-260 m/min), feed (0.08-0.2 mm/rev) and a constant cutting depth of 1 mm. The chip-tool interface temperature is measured using the tool-work thermocouple principle. The Calibration Setup is designed to establish the relationship between the produced electromotive force ( EMF ) and the cutting temperature during machining. Statistical dimensional analysis and arti fi cial neural network models have been developed to predict the temperature of the chip-tool interface. Tangential cutting force and chip attributes such as chip width and thickness are also measured depending on the cutting conditions, which is a prerequisite for dimensional analysis simulation. Results and Discussion. A tool made of TiAlN carbide with PVD coating had a lower temperature at the chip-tool interface than a tool with TiN / TiAlN coating. It has been observed that the chip-tool interface temperature increases prominently with the cutting speed, followed by the chip cross-sectional area and the speci fi c cutting pressure. However, a lower cutting force was observed when using a carbide tool with a multi-layer TiN / TiAlN coating, which can be attributed to a lower coef fi cient of friction created by the front surface of this tool for fl owing chips. On the other hand, the greatest cutting force was observed in uncoated carbide tools. It was noticed that the developed models allow predicting the temperature of the chip-tool interface with an absolute error of 5%. However, the lowest average absolute error of 0.78% was observed with the ANN model and, therefore, can be reliably used to predict the chip-tool interface temperature during SS304 turning. For citation: Kulkarni A.P., Chinchanikar S., Sargade V.G. Dimensional analysis and ANN simulation of chip-tool interface temperature during turning SS304. Obrabotka metallov (tekhnologiya, oborudovanie, instrumenty) = Metal Working and Material Science , 2021, vol. 23, no. 4, pp. 47–64. DOI: 10.17212/1994-6309-2021-23.4-47-64. (In Russian). ______ * Corresponding author Chinchanikar Satish , Ph.D. (Engineering), Professor Vishwakarma Institute of Information Technology, Pune, Maharashtra, India Tel.: 91-2026950441, e-mail: satish.chinchanikar@viit.ac.in Introduction Austenitic stainless steel, the most consumed nonmagnetic steel, is categorized under dif fi cult-to-cut materials. This is due to its tendency to produce long, sticky, and stringy chips along with the formation of the built-up edge during machining that produces less tool life and poor surface fi nish. The selection of