Algorithm Prediction for Finish Roughness in Milling Process for a Steel Aisi P20 with Thermal Treatment (Harden and Anneal) in a Range Hardness of 60-64 Hrc

The research presents an optimization of milling parameters in order to predict the final roughness of the product. The parameters such as cutting speed, feed rate, trajectory and radial depth for AISI P20 steel with thermal treatment hardening and tempering for machining in flat milling using CAD / CAM manufacturing processes were optimized. Taguchi and Anova methods were used to determine the equation that predicts the roughness. The article analyzed the parameter that has the most influence on finished surface. A statistical regression equation was obtained in order to predict the final roughness. The prediction will reduce manufacturing costs and optimize processes. The combinations of the parameters were obtained by using the orthogonal matrix L27 of the Taguchi method. The roughness data measured after machining was analyzed using the variance of the Anova method. Cutting speed parameter has the greater influence on roughness with a percentage of 66.19%. The ideal arrangement of the parameters allowed to find an optimal roughness of 0.11 μm. The arrangement was established by a cutting speed = 25 m / min, feed = 101.461 m / min, trajectory = ZIG-ZAG, radial depth = 1.29 mm. The values of the optimal roughness calculated showed better results in the final product. The results revealed that the Taguchi and Anova methods can calculate optimal values to obtain the best results of the roughness in flat milling.