Modeling the effect of variable work piece hardness on surface roughness in an end milling using multiple regression and adaptive Neuro fuzzy inference system

Desale, P.; Jahagirdar, R.

Growing Science » International Journal of Industrial Engineering Computations » Modeling the effect of variable work piece hardness on surface roughness in an end milling using multiple regression and adaptive Neuro fuzzy inference system

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International Journal of Industrial Engineering Computations

ISSN 1923-2934 (Online) - ISSN 1923-2926 (Print) Quarterly Publication Volume 5 Issue 2 pp. 265-272 , 2014

Modeling the effect of variable work piece hardness on surface roughness in an end milling using multiple regression and adaptive Neuro fuzzy inference system Pages 265-272 Download PDF

Authors: Purushottam S. Desale, Ramchandra S. Jahagirdar

DOI: 10.5267/j.ijiec.2013.11.005

Keywords: End Milling, Fuzzy inference system, Regression, Surface roughness, Tool steel

Abstract: The aim of this study is to correlate work piece material hardness with surface roughness in prediction studies. The proposed model is for prediction of surface roughness of tool steel materials of hardness 55 HRC to 62 HRC (±2 HRC). The machining experiments are performed under various cutting conditions using work piece of different hardness. The surface roughness of these specimens is measured. The result showed that the influence of work piece material hardness on surface finish is significant for cutting speed and feed in CNC end milling operation. It is also observed that the surface roughness prediction accuracy of Adaptive neuro fuzzy inference system using triangular membership function is better than Gaussian, bell shape membership function and regression analysis. Surface roughness prediction accuracy with material hardness as input parameter is 97.61%.

How to cite this paper

 Desale, P & Jahagirdar, R. (2014). Modeling the effect of variable work piece hardness on surface roughness in an end milling using multiple regression and adaptive Neuro fuzzy inference system.International Journal of Industrial Engineering Computations , 5(2), 265-272.

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