Surface roughness is considered as the quality index of the machine parts. Many diverse techniques have been applied in modelling metal cutting processes. Previous studies have revealed that artificial intelligence techniques are novel soft computing methods which fit the solution of nonlinear and complex problems like metal cutting processes. The present study used adaptive neurofuzzy inference system for the purpose of predicting the surface roughness when end milling Ti6Al4V alloy with coated (PVD) and uncoated cutting tools under dry cutting conditions. Real experimental results have been used for training and testing of ANFIS models, and the best model was selected based on minimum root mean square error. A generalized bell-shaped function has been adopted as a membership function for the modelling process, and its numbers were changed from 2 to 5. The findings provided evidence of the capability of ANFIS in modelling surface roughness in end milling process and obtainment of good matching between experimental and predicted results. 1. Introduction Ti6Al4V alloy is an important super alloy being subjected to diverse applications in the biomedical, aerospace, and chemical industries because of its properties and features such as high specific strength, high corrosion resistance, and good performance at high temperatures. However, since it has certain features such as its chemical reactivity, low thermal conductivity (this reduces heat dissipation from the cutting zone), high strength and hardness, and its low modulus of elasticity which make it more flexible than metals such as steel, Ti6Al4V alloy is regarded to be difficult to machine [1, 2]. Therefore, choosing the cutting parameters for end milling alloys such as this is considered as a critical process. High quality products at a reasonable cost have been increasingly demanded or required, which has compelled manufacturers into fierce competition. Thus, to evaluate the quality of products, it is important to consider surface finish as an important factor in such evaluation. The purpose of using surface roughness ( ) as an index is mostly to determine the surface finish in the machining process. In classifying modeling techniques for the prediction of , there are three major categories, namely, experimental models, analytical models, and artificial intelligence- (AI-) based models [3]. Development of the first two categories or groups is possible to be carried out by using conventional approaches such as the statistical regression technique. However, for developing the last category, AI-based
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