Investigation of Surface Roughness During Hot Air Streaming Turning Process of Different Materials and Optimization of Parameters Using Particle Swarm Optimization

Abstract

Different types of coolants are widely used in different metal cutting processes to improve the machining responses. But the suitability of using the correct cutting fluid is very important considering the concept of green environment. In this study, hot air is used as an alternative approach for hot machining process which is considered to initially heat the work-piece for easy machining operation. Firstly, two different velocities of hot air have been applied during the machining of mild steel in turning process. Next, the hot air has been kept at a fixed temperature and applied to three different materials (Brass, Aluminum and Stainless Steel) during turning operation keeping the other process parameters same. With the variation of different process parameters, it has been observed that surface roughness at different cutting conditions using hot air is improved significantly. A clear comparison has been made to investigate the responses of surface roughness at different cutting conditions in between the hot air and normal machining processes. Finally Particle Swarm Optimization (PSO), a relatively new, modern, and powerful method of optimization has been applied to perform well on these optimization problems and to find the global optimum solution in a complex search space. This thesis aims at providing a review and discussion of an established procedure which may be used as an alternative approach in the dry cutting research in the days to come as well as PSO algorithm which exposes the most active research topics that can give initiative for future work and help the practitioner improving better result with little effort.