Statistical analysis and multi-objective optimization of tungsten carbide alloy wirecut process using Taguchi method and genetic algorithm

Wire electrical discharge machining (WEDM) is one of the methods of producing tungsten carbide parts, which due to its poor machinability; optimal cutting with traditional methods is not possible. Material removal rate and surface roughness are the most important output indicators of the wire cut process. In this research, the Wire cut electrical discharge cutting process on tungsten carbide alloy has modeled and optimized. Mathematical modeling has used to establish an accurate relationship between input parameters and process outputs. In this regard, first, the necessary data collected by conducting an experiment, designed by the Design of experiments (DOE) by Taguchi method. Then the types of regression functions including linear and second order fitted for this data. In the next step, the validity of these models has been assessed using statistical hypothesis tests and analysis of variance. With the help of the proposed model, the parameters that have the greatest impact on the two outputs of material removal rate and surface roughness can be determined. After determining the appropriate models, the process optimization is performed using the genetic algorithm based on non-dominated sorting (NSGA-II) and the optimal combination of input parameters table is presented. Finally, a validation experiment performed with one of the compositions of the optimal table and the results of this experiment compared with the results obtained through optimization. Obtained results show good confirmation.