Multi-constrained optimization in ultrasonic-assisted turning of hardened steel by electromagnetism-like algorithm

Abstract

Ultrasonic-assisted machining is a method used to improve the machining technologies and overcome some problems in recent decades. To achieve optimal machining performance during hard turning, proper selection of turning parameters is considered as an important issue. The process becomes more complicated when methods such as ultrasonic-assisted machining are applied and ultrasonic vibration variables are also extracted. In this research work, a methodology is proposed to optimize ultrasonic-assisted turning process during machining of hardened steel AISI 4140. Neural network was employed to model process outputs (surface roughness and cutting force). Then electromagnetism-like algorithm was coupled to neural network to maximize the material removal rate with regard to outputs of process as constraints to achieve the machined part requirements. Experimental design technique was also used to obtain the needed experimental data for training neural network to predict process outputs. Material removal rate constitutes the main function of the electromagnetism algorithm; cutting force and surface roughness were applied as the constraints of the electromagnetism-like algorithm function. The unconstrained objective function that was created using penalty method was then optimized by the electromagnetism-like algorithm and genetic algorithm codes. It was observed that electromagnetism-like algorithm has more accurate results in comparison to genetic algorithm. Finally, the obtained optimum variables were experimentally tested in order to examine the mentioned method. Good compatibilities are observed between the values of optimization method and the experimental measurements.