Study the effects of milling parameters on surface roughness for Low Carbon Steel AISI 1015 | ||
Anbar Journal of Engineering Sciences | ||
Article 1, Volume 14, Issue 1, May 2023, Pages 1-6 PDF (2.39 M) | ||
Document Type: Research Paper | ||
DOI: 10.37649/aengs.2023.137203.1031 | ||
Author | ||
Rasha Qasim* | ||
Ministry of Education, Gifted Guardianship Committee, Baghdad, Iraq | ||
Abstract | ||
Milling includes a variety of different tasks and tools, ranging from small individual pieces to large, powerful group processes. It is one of the most commonly used techniques for producing custom parts with exact tolerances. Surface roughness of machined parts has a significant impact on the finished item's quality, which may have an impact on its tolerance and performance. This paper studies the prediction of the values of surface roughness of low-carbon steel AISI 1015 in milling operations. Three different machining parameters with nine variable samples are selected to investigate the resultant surface roughness of the AISI 1015 low-carbon steel samples, including different spindle speeds, feed rates, and depths of cut. The results revealed that the feed rate of 100 mm/min at a spindle speed of 930 rpm and a depth of 1.5 mm produced the lowest surface roughness (Ra) value of 1.170 µm, while the feed rate of 300 mm/min at a spindle speed of 1100 rpm produced the greatest surface roughness value of 2.605. | ||
Keywords | ||
Keywords: Milling Process; Surface Roughness; ANOVA; Taguchi | ||
References | ||
[1] D. Bhanu Prakash, "Optimization Of Machining Parameters For Aluminium Alloy 6082 In Cnc End Milling," vol. 3, pp. 505–510, 2013.
[2] J WILEY, Fundamentals of Modern Manufacturing, WILEY I.International Edition, 2002.
[3] F. Puh, Z. Jurkovic, M. Perinic, M. Brezocnik, and S. Buljan, "Optimization of machining parameters for turning operation with multiple quality characteristics using Grey relational analysis," Teh. Vjesn., vol. 23, no. 2, pp. 377–382, 2016.
[4] S. Sakthivelu, M. Meignanamoorthy, M. Ravichandran, and M. Kumar, "Effect of machining parameters on surface roughness and material removal rate in CNC end milling," Int. J. Sci., 2017.
[5] A. Zerti, M. A. Yallese, O. Zerti, M. Nouioua, and R. Khettabi, "Prediction of machining performance using RSM and ANN models in hard turning of martensitic stainless steel AISI 420," Proc. Inst. Mech. Eng. Part C J. Mech. Eng. Sci., vol. 233, no. 13, pp. 4439–4462, 2019.
[6] P. Palanisamy, I. Rajendran, and S. Shanmugasundaram, "Prediction of tool wear using regression and ANN models in end-milling operation," Int. J. Adv. Manuf. Technol., vol. 37, pp. 29–41, 2008.
[7] A. M. Zain, H. Haron, and S. Sharif, "Application of regression and ANN techniques for modeling of the surface roughness in end milling machining process," in 2009 Third Asia International Conference on Modelling & Simulation, 2009, pp. 188–193.
[8] K. Khalil, A. Mohd, C. O. C. Mohamad, Y. Faizul, and S. Z. Ariffin, "The Optimization of Machining Parameters on Surface Roughness for AISI D3 Steel," in Journal of Physics: Conference Series, 2021, vol. 1874, no. 1, p. 12063.
[9] L. Nisar, B. Banday, M. Amatullah, M. Farooq, A. N. Thoker, A. Maqbool, and M. A. Wahid, "An investigation on effect of process parameters on surface roughness and dimensional inaccuracy using Grey based Taguchi method," Mater. Today Proc., vol. 46, pp. 6564–6569, 2021.
[10] L. H. Kashkool, "Optimization of Machining Parameters of AISI 1045 Steel for Better Surface Finish and Tool Life Using TiN Coated Carbide Insert," Tikrit J. Eng. Sci., vol. 29, no. 2, pp. 1–6, 2022.
[11] U. Parametrov, "Optimization of the machining parameters for the turning of 15-5 PH stainless steels using the Taguchi method," Optimization, vol. 133, p. 140, 2017.
[12] S. Karabulut, "Optimization of surface roughness and cutting force during AA7039/Al2O3 metal matrix composites milling using neural networks and Taguchi method," Meas. J. Int. Meas. Confed., vol. 66, pp. 139–149, 2015. | ||
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