Predict the Best Variants of Cutting in Turning Process
Using Genetic Algorithm Technique
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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. Moreover, 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, b...
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. Moreover, 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.
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