Application Potential of Fuzzy and Regression in Optimization of MRR and Surface Roughness during Machining of C45 Steel

  • Santosh Madival School of Mechanical Engineering, REVA University, Yelahanka, Bangalore-560064, India
  • Manjunath Lingappa Halappa School of Mechanical Engineering, REVA University, Yelahanka, Bangalore-560064, India
  • Mohammed Riyaz Ahmed School of Electronics and Communication Engineering, REVA University, Yelahanka, Bangalore-560064, India
  • Lokesha Marulaiah Department of Mechanical Engineering, Mangalore Institute of Technology and Engineering, Mangalore-574225, Moodbidri, India

Abstract

In the machining industry, coolant has an important role due to their lubrication, cooling and chip removal functions. Using coolant can improve machining process efficiency, tool life, surface quality and it can reduce cutting forces and vibrations. However, health and environmental problems are encountered with the use of coolants. Hence, there has been a high demand for deep cryogenic treatment to reduce these harmful effects. For this purpose, −196 °C LN2 gas is used to improve machining performance. This study focuses on the prediction of surface roughness and material removal rate with cryogenically treated M2 HSS tool using fuzzy logic and regression model. The turning experiments are conducted according to Taguchi's L9 orthogonal array. Surface roughness and material removal rate during machining of C45 steel with HSS tool are measured. Cutting speed, feed rate, and depth of cut are considered as machining parameters. A model depended on a regression model is established and the results obtained from the regression model are compared with the results based on fuzzy logic and experiment. The effectiveness of regression models and fuzzy logic has been determined by analyzing the correlation coefficient and by comparing experimental results. Regression model gives closer values to experimentally measured values than fuzzy logic. It has been concluded that regression-based modeling can be used to predict the surface roughness successfully.

Keywords: surface roughness, material removal rate, regression, fuzzy logic
Published online
2019-03-18
How to Cite
Madival, S., Halappa, M., Ahmed, M. and Marulaiah, L. (2019) “Application Potential of Fuzzy and Regression in Optimization of MRR and Surface Roughness during Machining of C45 Steel”, Periodica Polytechnica Mechanical Engineering, 63(2), pp. 132-139. doi: https://doi.org/10.3311/PPme.13171.
Section
Articles