Analysis of cutting parameters on tool wear in turning of Ti-6Al-4V alloy by multiple linear regression and genetic expression programming methods

Abstract

Titanium alloys are materials difficult to machine due to their excellent characteristics. In the machining process of these alloy materials, it is critical to determine the important coefficient cutting parameters such as depth of cut (ap), cutting speed (Vc), feed rate (fr), and cutting length (Lc) on tool wear (VB), and to simplify them by creating a mathematical expression. The effects of cutting parameters on VB were explored experimentally, in this study, by turning the Ti-6Al-4V alloy at various cutting parameters. Multiple linear regression (MLR) and Genetic Expression Programming (GEP) methods were used to examine the acquired data. The mathematical expression derived from GEP was simplified. Increasing the cutting parameter values resulted in an increase in VB. The analyses revealed that the parameter of Vc had a considerable impact on VB. The best performance (R2) from MLR analyzes was determined as 0.705. This performance was computed as 0.896 in stepwise MLR (SMLR) and 0.96 in GEP. The performance value was found to be 0.911 by simplifying the mathematical expression derived from GEP. It was observed that the analysis of parameter values of ap, Vc, fr and Lc on estimation performance of VB with the proposed GEP method was appropriate.