Predict the Best Variants of Cutting in Turning Process Using Genetic Algorithm Technique

摘要

An optimization based on genetic algorithm (GA) for determining the cutting parameters in machining operations is proposed. In turning metal cutting processes, cutting conditions are influencing the tool wear and material removal rate. The genetic algorithm has been used as an optimal solution tool in order to find optimal cutting parameters during a turning process. More over, process optimization has to yield minimum tool wear, tool life, and maximum material removal rate. The material that selected for the machining is EN24T steel, since it's used in different applications such as rollers, bolts, screws and connecting rods. The turning operation is implemented on CNC lathe with SINUMERIK 802D in order to study the performance characteristics for turning of EN24T Steel by taking coated carbide inserts cutting tool. Furthermore, the analysis of variance (ANOVA)is applied to find the significant input parameters which will mostly affect the output responses. Since the genetic algorithm-based approach can obtain the near-optimal solution, it can be used for machining parameter selection of machined parts that require many machining constraints. The main advantage of the proposed methodology is the capability to perform multi-object optimization. The results obtained from the GA model have presented a fast and suitable solution for automatic selection of the machining parameters. Finally, it can be concluded from the results of this work that the optimal value of cutting speed is (296.100 m/min), depth of cut is (1.33 mm) and feed is (0.4 mm/rev).