Investigating the Effect of Tool and Work Material in Ultrasonic Machining of Titanium Alloys

Abstract

By way of the development of technology, the scientists and technologists in the field of manufacturing are facing more and more challenges. Technologically advanced industries such as aeronautics, nuclear reactors and automobiles have been demanding high strength temperature resistant (HSTR) materials having high strength to weight ratio. Ultrasonic Machining (USM) is one of the non-traditional machining processes that are widely used in commercial machining of hard and brittle materials such as ceramics, refractory materials and precision stones. USM is a process known for its capabilities in providing excellent surface finish without any significant alterations is surface integrity or structure of the work material. Moreover, the compressive stress induced in the sub-surface as a result of repeated impacts of abrasive grains contributes in improving the fatigue strength of the machined components; which is a very important aspect especially for a material like titanium and its alloys. Hence, the study was aimed to investigate the machining characteristics of commercially pure titanium (ASTM Grade-2) and its alloys (ASTM Grade-5) as work material, in combination with three different tool materials (High carbon steel; High speed steel and Stainless steel) with cryogenic treatment and non-cryogenic treatment of tool in ultrasonic machining and to model these characteristics for their application in the concerned manufacturing industry. The machining characteristics investigated are material removal rate (MRR); tool wears rate (TWR); micro hardness and surface roughness. The effect of various input parameters on output responses have been analyzed using Analysis of Variance (ANOVA).Microstructure analysis has been completed to understand the form and amount of deposition on the surface of the workpiece material. Main effect plot and interaction plot for significant factors and S/N ratio have been used to determine the optimal design for each output response. Grey relational analysis and Genetic Algorithms were used to determine the optimum combination of input parameters to get the best results.