To optimize the surface roughness and microhardness of β-Ti alloy in PMEDM process using Non-dominated Sorting Genetic Algorithm-II

The success of implant depends upon the surface characteristics like roughness, topography, chemistry and surface hardness. The fabrication of hard surface in combination with micron-/submicron- and nano-scale surface roughness is a great challenge for bio-manufacturing industries. Specifically, the surface micro-hardness (SMH) needs to be maximized while controlling the surface roughness (SR). In the present study, an attempt has been made on the application of Non-dominated Sorting Genetic Algorithm (NSGA)-II to predict the optimal conditions of powder mixed electric discharge machining parameters to maximize the SMH and to minimize the SR. The experiments were performed on a beta phase titanium alloy (β-Ti) workpiece at a self developed powder mixed electric discharge machining (PM-EDM) set-up. All the experimental results were used to develop the mathematical model using Taguchi based response surface methodology (RSM). The developed model was used to optimize the process parameters of PM-EDM process using NSGA-II. Finally, optimal solutions obtained from Pareto front are presented and compared with experimental data. The best optimal condition was 13 A peak current, 5 μs pulse duration, 8% duty cycle (longer pulse-interval) and 8 g/l silicon powder concentration for achieving a required low SR and high SMH.