Developments in cutting tools and machine tools have made it possible to cut materials in their hardened state. Inconel 718 superalloy is known as a difficult-to-cut material although is the most widely used superalloy, accounting for approximately one-third of all superalloy production.. Typical aerospace applications include compressors and turbine disks, buckets and spacers and bolts for jet engines, liquid rocket components involving cryogenic temperatures and power supply batteries for satellites. Since turning is the primary operation in most of the production processes in the industry, surface finish of turned components has greater influence on the quality of the product. Surface finish in turning has been found to be influenced in varying amounts by a number of factors such as feed rate, work material characteristics, work hardness, unstable built-up edge, cutting speed, feed rate, cutting time, tool nose radius and tool cutting edge angles, stability of machine tool and workpiece- setup, chatter, and use of cutting fluids. Like other Ni-base superalloys, the machinability of Inconel 718 is inferior to that of most steels including stainless steels. The main goal of this study is to improve the conditions of turning of Inconel 718 superalloy in order to minimize amount of flank wear and maximize the surface finish. Several methods of research have been used in this study. In the first stage, someexperimentwere performed based on full factorial design using a TiAlN coated carbide tool, GC 1105.. The designing parameters include cutting velocity, feed rate, nose radius, and entering angle. In addition, the responses are the maximum flank wear, VBmax, and surface roughness, Ra. The second step is nonlinear modeling of the process by artificial neural networks.. In order to obtain the relation between each of the outputs and the designing parameters, an artificial neural networks was trained. The third step is the optimization of turning process by genetic algorithm. In this phase, the maximum flank wear as the objective function and the surface roughness as the nonlinear constraint function have been picked up. Finally, the optimized parameters resulted from combination of A and GAs have been evaluated by a validation test. Key words: Turning; Inconel 718; Design of Experimental; Genetic algorithm; Artificial neural network;
