A comprehensive optimization of ultrasonic burnishing process regarding energy efficiency and workpiece quality.

In the present work, optimization attempt based on artificial intelligence has been carried out to minimize the energy consumption of ultrasonic burnishing process subjected to specified range of surface roughness, arc height and hardness. Optimization has been carried out by association of adaptive network based fuzzy inference system (ANFIS) and particle swarm optimization (PSO). It is found from the optimization results that obtaining minimum energy as well as desired quality characteristic is not possible at same time. There is a threshold weight ratio where the energy consumption and workpiece quality are seriously undermined after and before this point, respectively. Hence, this threshold corresponds to the optimum solution of the process. The optimum results have been further verified through confirmatory experiments and there is good correlation between both approaches. • Ultrasonic assisted surface burnishing has been carried out on AA6061-T6 alloy. • The energy consumption, surface roughness, arc height and hardness have been modeled by adaptive network based fuzzy inference system • Multi-objective optimization has been carried out by particle swarm optimization algorithm [ABSTRACT FROM AUTHOR]