Research on the prediction model of micro-milling surface roughness of Inconel718 based on SVM

Abstract

Purpose – The purpose of this paper is to establish a roughness prediction model of micro-milling Inconel718 with high precision. Design/methodology/approach – A prediction model of micro-milling surface roughness of Inconel718 is established by SVM (support vector machine) in this paper. Three cutting parameters are involved in the model (spindle speed, cutting depth and feed speed). Experiments are carried out to verify the accuracy of the model. Findings – The results show that the built SVM prediction model has high prediction accuracy and can predict the surface roughness value and variation law of micro-milling Inconel718. Practical implication – Inconel718 with high strength and high hardness under high temperature is the suitable material for manufacturing micro parts which need a high strength at high temperature. Surface roughness is an important performance indication for micro-milling processing. Establishing a roughness prediction model with high precision is helpful to select the cutting parameters for micro-milling Inconel718. Originality/value – The built SVM prediction model of micro-milling surface roughness of Inconel718 is verified by experiment for the first time. The test results show that the surface roughness prediction model can be used to predict the surface roughness during micro-milling Inconel718, and to provide a reference for selection of cutting parameters of micro-milling Inconel718.