Parameter Optimization of Ultrasonic Machining Process Using Nontraditional Optimization Algorithms

The optimum selection of process parameters is essential for advanced machining processes, as these processes incur high initial investment, tooling cost, and operating and maintenance costs. This article presents optimization aspects of an important advanced machining process known as ultrasonic machining (USM). The objective considered is maximization of material removal rate (MRR) subjected to the constraint of surface roughness. The process variables considered for optimization are amplitude of ultrasonic vibration, frequency of ultrasonic vibration, mean diameter of abrasive particles, volumetric concentration of abrasive particles, and static feed force. The optimization is carried out using three nontraditional optimization algorithms, namely, artificial bee colony (ABC), harmony search (HS), and particle swarm optimization (PSO). An application example is presented and solved to illustrate the effectiveness of the presented algorithms. The results of the presented algorithms are compared with the previously published results obtained by using genetic algorithm (GA).