Enhancing post machining surface finish of titanium alloy by cutting parameter optimization using ANOVA analysis

Titanium alloys are categorized under difficult to machine materials. The machinability of titanium alloy Ti6Al4V using statistical methods such as analysis of variance is investigated in this paper. Titanium alloy Ti6Al4V is the most widely used in aerospace and biomedical application due to its advantageous material properties. However, despite its wide-ranging applications, there is a lack of clarity concerning its ideal machining parameters. This ambiguity primarily stems from titanium alloys’ inherent properties, notably their low thermal conductivity and high chemical reactivity. Understanding and optimizing the machining parameters to get the right combination of speed, feed, depth of cut, and coolant condition are vital. Furthermore, to decipher the collected data and interpret the results, analysis of variance techniques were utilized with the help of software R-programming. The insights garnered can lead to more streamlined machining strategies, ensuring higher productivity and efficiency. By bridging the knowledge gap, this research seeks to make machining titanium alloys simpler, cost-effective, and more efficient for manufacturers. The paper output shows that the mean square values range from nearly zero for cutting speed, feed rate, and depth of cut to around 11.817 for coolant respectively. The paper concludes with the various P-values obtained for the cutting parameters influencing the surface roughness using the analysis of variance technique. The effect of coolant on the surface roughness has been significant with a P-value of 0.000117.