Prediction of surface roughness in hard machining of EN31 steel with TiAlN coated cutting tool using fuzzy logic

Abstract

Hardened EN31 steel is extensively used to make parts in engineering industries. Hardened steel, due to its special properties is difficult to machine. Cemented carbide, coated tools, ceramics and cubic boron nitride are employed to machine EN31 steel. Choosing the best combination of cutting parameters for good surface finish is a complex task in hard machining process. In this study, Fuzzy logic is employed to predict surface roughness in hard machining of EN31 steel using TiAlN coated cutting tool. Nine metal cutting experiments were performed with three levels of cutting speed, feed rate and depth of cut. Surface finish was measured with a surface roughness tester. For developing the fuzzy logic models, the MATLAB R2020b software was used. The built-in fuzzy logic toolbox was used for building the model. The inference system selected for this model was madman. Triangular membership function is assigned to all the parameters and variables. Nine fuzzy rules were created in linguistic form using the data derived from the machining experiments. It can be concluded that fuzzy logic model is able to estimate surface roughness with acceptable accuracy. High cutting speed and low feed rates delivers good surface finish.