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Turning operation, a type of machining process using Computer Numerical Control (CNC) machine in which a cutting tool, typically a non-rotary tool bit, moves to describe a helix toolpath while the cylindrical metal workpiece rotates. Numerous conflicting performance functions such as maximizing material removal rate, minimizing the product’s quality, maximizing the tool life and others, remains crucial for a system to optimize in order to obtain optimum benefit. The machinist is required to assign the optimal cutting parameters in CNC turning machine which have direct influence on the performance of each cutting process and machined product. It is very crucial for optimal parameters selection to maximize the performance function. A new optimisation model has been proposed in this paper. This model, uses Box Behnken Design (BBD) for design of experiment and the prediction model has been developed using Extreme Learning Machine (ELM) which is tuned using Particle Swarm Optimization. A powerful and effective, Multi Objective Genetic Algorithm (MOGA) will act as an optimizer of the developed model. Turning input parameters such as feed rate, cutting speed and depth of cut were considered as input variables and surface roughness, specific power consumption and cutting force were used as output variables. This novel approach, BBD-ELM-PSO-MOGA can predict the optimal cutting parameters as demonstrated in our case studies with less number of tunable parameters and number of experiments. Therefore, it is fast, less time consuming and easy to be implemented.

Multi Objective Optimization for Turning Operation using Hybrid Extreme Learning Machine and Multi Objective Genetic Algorithm