A calculation of surface roughness depending on the axial feed rate and tool nose radius when turning the 40x steel
DOI:
https://doi.org/10.14419/ijet.v7i4.29568Published:
2019-07-22Keywords:
Calculating Surface Roughness, 40X Steel, CNC Turning Machine, Insert Nose Radius.Abstract
This paper presents the experimental investigation of the surface roughness in turning process of 40X steel with the different insert nose radius and different axial feed rate. By analyzing the experimental results, the appropriate formula was proposed to calculate surface roughness when turning 40X steel with different value of the insert nose radius and different axial feed rate. The most suitable regression of surface roughness was an exponential regression with the confidence level is more than 97.3 %. These formulas were successfully verified by experimental results with very promising results. In comparison of predicted results, the results from previous research, and the measured results, the results of proposed formulas gave the smallest errors (9.73 % in case insert nose radius is 0.2 mm and 1.46 % in case insert nose radius is 0.4 mm). The approach method of the present study can be applied in industrial machining to improve the surface quality in turning processes of the X40 Steel.
References
[1] N.R. Abburi and U.S. Dixit (2006), A knowledge-based system for the prediction of surface roughness in turning process. Robotics and Computer-Integrated Manufacturing 22, 363–372. https://doi.org/10.1016/j.rcim.2005.08.002.
[2] Baris Buldum, Aydın ŞIK, Ali Akdagli and Mustafa Berkan Biçer (2017), ANN surface roughness prediction of AZ91D magnesium alloys in the turning process. Materials Testing 59, 916-920. https://doi.org/10.3139/120.111088.
[3] Jean-Philippe Costes (2013), A predictive surface profle model for turning based on spectral analysis. Journal of Materials Processing Technology, Elsevier, 213, Issue 1, 94-100. https://doi.org/10.1016/j.jmatprotec.2012.08.009.
[4] B. Sidda Reddy, J. Suresh Kumar, K. Vijaya Kumar Reddy (2009), Prediction of Surface Roughness in Turning Using Adaptive Neuro-Fuzzy Inference System. Jordan Journal of Mechanical and Industrial Engineering, Volume 3, Number 4, 252 – 259.
[5] G. Boothroyd and W.A. Knight (1989), Fundamental of Machining and Machine Tool. Marcel Dekker, New York.
[6] J-E. Ståhl, F. Schultheiss, and S. Hägglund (2011), Analytical and Experimental Determination of the Ra Surface Roughness during Turning. Procedia Engineering 19, 349 – 356. https://doi.org/10.1016/j.proeng.2011.11.124.
[7] Groover, M. P (1996), Fundamentals of Modern Manufacturing. Prentice Hall, Upper Saddle River, NJ.
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Accepted 2019-07-10
Published 2019-07-22