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

Lokesha Marulaiah,Manjunath Lingappa Halappa,Mohammed Riyaz Ahmed,Santosh Madival

doi:10.3311/ppme.13171

Lokesha Marulaiah, Manjunath Lingappa Halappa + Show 2 more

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https://doi.org/10.3311/ppme.13171

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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.

Highlights

In the manufacturing industry, machining process plays an important role
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
4 Conclusions In this research, the model depends on the regression model approach on the forecast of machining parameters and the results obtained from the regression model was correlated with the results based on experiment and fuzzy logic

Summary

Introduction

In the manufacturing industry, machining process plays an important role. Machining is defined as the removal of excess material from the workpiece in the form of chips. The surface roughness (Ra) is an essential evaluation of the quality product since it considerably affects the staging of mechanical parts as well as the cost of production. Quality of surface (roughness) has a link with the mechanical properties such as corrosion resistance, fatigue behavior. HSS tool finds its application as a milling cutter, bits, drills, and broaches. When a too low cutting speed of bits is required, a manufacturer will think of carbide tools [1]. A standard procedure used in the ancient times to enhance the life of HSS cutting tools is by subjecting them to heat treatment, which gives higher control beyond the scope of attributes viz. A standard procedure used in the ancient times to enhance the life of HSS cutting tools is by subjecting them to heat treatment, which gives higher control beyond the scope of attributes viz. excellent wear resistance, hardness, and excellent toughness

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