Mathematical modeling and optimization of surface roughness in turning of polyamide based on artificial neural network

Abstract

This paper presents the methodology of mathe-matical modeling of surface roughness in turning of poly-amide based on artificial neural network. The surface roughness model was developed in terms of the main cut-ting parameters such as feed rate, cutting speed, depth of cut, and tool nose radius. The data for modeling were col-lected through experiment based on Taguchi L27 orthogo-nal array. In addition to modeling, by applying the simplex optimization method, the optimal cutting parameter setting minimizing surface roughness, was determined. From the model analysis performed by generating 3D response graphs the following conclusions are drawn. Feed rate is the dominant factor affecting surface roughness, followed by tool nose radius and depth of cut. As for cutting speed, its effect is not very important. The minimal surface roughness results with the combination of low feed rate, low depth of cut, low cutting speed and high tool nose radius.