OPTIMIZATION OF CUTTING PARAMETER FOR EFFICIENT ENERGY IN CNC MILLING MACHINE - A REVIEW

Energy efficiency is one of the main drivers for achieving sustainable manufacturing. Advances in machine tool design have reduced the energy consumption of such equipment, but still machine tools remain one of the most energy demanding equipment in a work shop. This study presents a novel approach aimed to improve the energy efficiency of machine tools through the online optimization of cutting conditions. The study is based on an industrial CNC controller with smart algorithms optimizing the cutting parame ters to reduce the overall machining time while at the same time minimizing the peak energy consumption. In the current trends of optimizing machining process parameters, various evolutionary or meta - heuristic techniques such as Particle Swarm Optimizatio n (PSO), Genetic Algorithm (GA), Simulated Annealing (SA), Ant Colony Optimization (ACO) and Artificial Bee Colony algorithm (ABC) have been used. This paper gives an overview of PSO techniques to optimize machining process parameter of both traditional an d modern machining from 2007 to 2011. Machining process parameters such as cutting speed, depth of cut and radial rake angle are mostly considered by researchers in order to minimize or maximize machining performances .