Author : Tanpure Sandesh Popat 1
Date of Publication :23rd June 2022
Abstract: In the automotive industry, machining is an essential part of the production process. On mild steel, a turning process was utilised to create shafts of varied diameters. This work attempts to optimise multiple factors such as surface roughness, MRR, and tool wear during turning operations on various steel grades. The average surface roughness (Ra) is one of the most important measurements of surface quality during the machining process, and it is primarily influenced by a number of machining parameters such as true rake angle and side cutting edge angle, cutting speed, feed rate, depth of cut, nose radius, and machining time. The Taguchi technique was used to build a surface roughness model to study how machining factors such as feed rate, tool geometry, nose radius, and machining duration impact the roughness of the surface created during the dry turning phase. This study demonstrates how to determine surface roughness values for CNC turning of AISI 52100 steel with varying coated tool nose radius using tribiological parameters. The test was carried out on a commercial CNC machine with coated tool nose radiuses of 0.4, 0.8, and 1.2 mm. Further investigation revealed that the CNC machine's design had been completed in Solid Works. Static and modal analyses were carried out using ANSYS Workbench. A static structural analysis of a CNC machine was carried out using 1G lading, which met the minimum Yield Strength requirement.
- Olugboji Oluwafemi Ayodeji et al., “In the lathe turning process, the effect of cutting speed and feed rate on tool wear rate and surface roughness”, International Journal of Engineering Trends and Technology (IJETT) – Volume22 Number 4- April 2015.
- Sachin Chauhan et al., “Study on Optimization of Turning Parameters on Various Steel Grades: A Review”, International Journal of Emerging Technologies in Engineering Research (IJETER), Volume 6, Issue 5, May (2018).
- Nexhat Qehaja, Kaltrine Jakupi et al., “Effect of Machining Parameters and Machining Time on Surface Roughness in Dry Turning Process”, 25th DAAAM International Symposium on Intelligent Manufacturing and Automation, DAAAM 2014.
- Sayyed Siraj et al., “Modeling of Roughness Value from Tribiological Parameters in Hard Turning of AISI 52100”, the 2nd International Conference on Materials Manufacturing and Design Engineering. 10.1016/j.promfg.2018.02.05.
- Abhang, L. B, and Hameedullah, M., 2014, “Parametric investigation of turning process on EN-31 steel”, Procedia Materials Science, (6), 1516-1523.
- Rao, C. R. P, Bhagyashekar, M. S, and Narendraviswanath, 2014, “Effect machining parameters on the surface roughness while turning particulate composites”, Procedia Engineering, (97), 421-431.
- Qehaja, N, Jakupi, K, Bunjaku, A, Bruçi, M, and Osmani, H., 2015, “Effect of machining parameters and machining time on surface roughness in dry turning process”, Procedia Engineering, (100), 135- 140.
- B sidda reddy, G.Padmanabhan and K.Vijay Kumar Reddy, “Surface roughness prediction technique for CNC technique”. Asian journal of scientific research 1 (3): 256-264, 2008 ISSN 1992-1454.
- Jitendra Thakkar et al., “A Review on Optimization of Process Parameters for Surface Roughness and Material Removal Rate for SS 410 Material During Turning Operation”, Int. Journal of Engineering Research and Applications ISSN : 2248-9622, Vol. 4, Issue 2( Version 1), February 2014, pp.235-242.
- Tian-Syung Lan, “Parametric Deduction Optimization for Surface Roughness”. American Journal of Applied Sciences 7 (9): 1248-1253, 2010 ISSN 1546-9239.
- C. M. Allen and B. Boardman, “ASM handbook, volume 1, properties and selection: irons, steels, and high performance alloys section: publication information and contributors publication information and contributors,” Fonderie, vol. 1, p. 1618, 2005.
- S. Chinchanikar and S. K. Choudhury, “Machining of hardened steel-experimental investigations, performance modeling and cooling techniques: a review,” International Journal of Machine Tools and Manufacture, vol. 89, pp. 95– 109, 2015.
- Explain Cutting Tools Archived 2019-05-12 at the Wayback Machine,
- Muammer Nalbant, Hasan Gokkaya, and ˙Ihsan Toktas, “Comparison of Regression and Artificial Neural Network Models for Surface Roughness Prediction with the Cutting Parameters in CNC Turning”, Hindawi Publishing Corporation Modelling and Simulation in Engineering, Volume 2007, Article ID 92717, 14 pages.
- V Durga Prasada Rao et al., “Optimization of cutting parameters in CNC turning of stainless steel 304 with TiAlN nano coated carbide cutting tool”, IOP Conf. Series: Materials Science and Engineering 310 (2018) 012109 doi:10.1088/ 1757-899X/310/1/012109.
- Rajaguru et al., “Coated tool Performance in Dry Turning of Super Duplex Stainless Steel”, 45th SME North American Manufacturing Research Conference doi: 10.1016/j .promfg.2017.07.061.
- C.O. Izelu et al., “Effect of Depth of Cut, Cutting Speed and Work-piece Overhang on Induced Vibration and Surface Roughness in the Turning of 41Cr4 Alloy Steel”, International Journal of Emerging Technology and Advanced Engineering