Optimization of the Internal Roller Burnishing Process for Energy Reduction and Surface Properties

Improving the surface quality after burnishing operation has been subject of various published investigations. Unfortunately, trade-off analysis between energy consumption and characteristics internal not addressed due to expensive cost huge efforts required. The objective present work is optimize factors, including spindle speed, feed, depth penetration, number rollers for minimizing consumed in time, as well roughness maximizing Rockwell hardness. An adaptive neuro-based-fuzzy inference system (ANFIS) was used develop objectives terms machining parameters. optimization outcomes were selected using an evolution algorithm, specifically non-dominated sorting particle swarm (NSPSO). results proposed ANFIS models are significant can be employed predict response values industrial applications. technique comprising NOPSO a powerful approach model performances select optimal parameters compared trial error method operator experience. Finally, solution help achieve improvements by 16.3 %, 24.3 hardness 4.0 common values.