Optimization of interior roller burnishing process for improving surface quality

Improving the surface characteristics of roller burnishing processes is one of effective approaches to decrease the machining costs and time. This paper systematically investigates the nonlinear relationships between machining parameters and surface characteristics, including surface roughness (Ra), surface hardness (H), and hardness depth (HD) of the interior roller burnishing using response surface method (RSM) model. Three process parameters considered include spindle speed S, feed rate F, and burnishing depth D. A set of physical experiments was carried out with AISI 1045 steel on a computer numerical control (CNC) milling machine using the roller burnishing tool. The target of the current complex optimization is to enhance the surface hardness and hardness depth, while the surface roughness is considered as the constraint. Finally, an evolutionary algorithm entitled archive-based micro genetic algorithm (AMGA) was used to generate a set of feasible optimal solutions and determine the best machining conditions. The results show that an appropriate trade-off solution can be drawn with regard to the low surface roughness and high the surface hardness as well as hardness depth. Furthermore, the integration of RSM model and AMGA can be considered as a powerful approach for modeling and optimizing interior roller burnishing processes.