EXPERIMENTAL SURFACE QUALITY ESTIMATION IN ULTRASONIC VIBRATION ASSISTED HELICAL MILLING

Authors:

V. Uma Sai Vara Prasad ,K. Venkata Rao,Ch. Nagraju ,M. Venu,M. Venkataiah,

DOI NO:

https://doi.org/10.26782/jmcms.2020.07.00041

Keywords:

Surface quality ,ultrasonic vibration,ANOVA,Helical milling,

Abstract

Surface quality is a vital aspect  to assess the eminence of products that chooses wear and also stimuli quality of assemblies. The research journal article is focused to estimate the surface quality during helical milling with ultrasonic vibration assistance to workpiece. This study presents an investigation of surface eminence on ultrasonic machining (UM) of difficult to cut material of D2 steel, an effort was  made for modeling response i.e. surface roughness(Ra) in UM technique by means of DESIGN EXPERT software. Three operational factors i.e. spindle speed(N), axial depth(ap)  each at two levels and orbital speed(nc) of four levels were  varied to investigate surface quality variations with respect to levels of operational factors. The ANOVA was performed to ascertain importance of model established. The testings outcomes confirms validity and competence of model developed.

Refference:

Bhuvnesh Bhardwaj, Rajesh Kumar, Pradeep K. Singh, et al., “Surface roughness prediction model for turning of AISI 1019 steel using response surface methodology and Box-Cox transformation”, Proc. Inst. Mech. E Part B: J. Eng. Manuf. 228 (2) (2014) 223–232.
II. C. Sanjay, C. Jyothi, “A study of surface roughness in drilling using mathematical analysis and neural networks”, International Journal of Advanced Manufacturing Technology, vol.29, pp. 846–852, 2006.
III. D. Dinakaran, S. Sampathkumar, K. Madhivanan, “An experimental investigation of surface roughness monitoring in surface grinding through ultrasonic technique”, Mechatronics and Intelligent Manufacturing, vol.1, pp.107-118, 2012.
IV. M. Subramanian, M. Sakthivel, K. Sooryaprakash, R. Sudhakaran, “Optimization of end mill tool geometry parameters for Al7075-T6 machining operations based on vibration amplitude by response surface methodology”, Measurement 46 (2013) 4005–4022.
V. Nitesh Dhar Badgayan, Ankan Mishra, Sameer Panda, “Prediction of Surface Roughness on Ultrasonic Machining Of Titanium Using Response Surface Methodology”, Proceedings of the ICIET’14, Volume 3, Special Issue 3, March 2014, Tamilnadu, India, pp. 1234-1236.
VI. Q. Zhao, X. Qin, C. Ji, Y. Li, D. Sun, Y. Jin, “Tool life and hole surface integrity studies for hole-making of Ti6Al4V alloy”, Int. J. Adv. Manuf. Technol. 79 (5–8) (2015) 1017–1026.
VII. R. Iyer, P. Koshy, E. Ng, “Helical milling: an enabling technology for hard machining precision holes in AISI D2 tool steel”, Int. J. Mach. Tool Manufact. 47 (2) (2007) 205–210.
VIII. R.B.D. Pereira, L.C. Brandão, A.P. de Paiva, J.R. Ferreira, J.P. Davim, “A review of helical milling process”, Int. J. Mach. Tool Manufact. 120 (2017) 27–48.
IX. Sanjay, Prithvi, “Hybrid intelligence systems and artificial neural network (ANN) approach for modeling of surface roughness in drilling”, Cogent Engineering, vol.1 (1), 2014.
X. Vishy Karri, Tossapol Kiatcharoenpol, “Prediction of internal surface roughness in drilling using three feed forward neural networks – a comparsion”, Proceedings of the 9th International Conference on Neural Information Processing (ICONIP’OZ) , vol. 4,2003.
XI. X. Qin, L. Gui, H. Li, B. Rong, D. Wang, H. Zhang, G. Zuo, “Feasibility study on the minimum quantity lubrication in high-speed helical milling of Ti-6Al-4V”, J. Adv. Mech. Design, Systems Manufacturing 6 (7) (2012) 1222–1233.

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