OPTIMIZATION OF ELECTRO-CHEMICAL MACHINING PROCESS PARAMETERS USING GENETIC ALGORITHMS

Abstract

ECM and ECM-based processes (derived and hybrid processes) are one of the most widely used advanced machining processes (AMPs) to make complicated shapes of varying sizes in the products made of electrically conducting but difficult-to-machine materials such as superalloys, Ti-alloys, alloy steel, tool steel, stainless steel, etc. These materials are extensively used in aerospace, automobile, space, nuclear, defense, cutting tools, dies and mold making applications. ECM offers some unique advantages over other conventional and advanced machining processes but its use incurs relatively higher initial investment cost, operating cost, tooling cost, and maintenance cost. Use of optimum ECM process parameters can significantly reduce the ECM operating, tooling, and maintenance cost and will produce components of higher accuracy which is very important in some critical areas such as aerospace, space, defense, nuclear applications. Therefore, choice of optimum process parameters is essential to ensure the most cost-effective, efficient, and economic utilization of ECM process potentials. This paper describes optimization of three most important ECM process parameters namely tool feed rate, electrolyte flow velocity, and applied voltage with an objective to minimize geometrical inaccuracy subjected to temperature, choking, and passivity constraints using real-coded genetic algorithms. Comparison of the obtained optimization results with the results of past work in this direction shows an improvement in terms of geometrical accuracy.