Design and Optimization of Iron Cow Stem with Flaps by Finite Element Method and Genetic Algorithm

Reversible plows are one of the important and efficient tools in primary tillage, which are affected by many dynamic loads. These tools are damaged in different working conditions, and they are damaged from the stem area. For this reason, in this research, a method based on the finite element method and genetic algorithm was presented to optimize the reversible plow shaft. In this research, two parameters of cross-sectional area and stem curvature were investigated as independent variables. A total of 24 different models for the plow shaft were designed in SolidWorks software, FEM software and used Iron Cow Stem. Then, the different designs of the stem in the environment of the abacus were loaded and stress free occurred in them and were eliminated. Then, using an artificial neural network, a model was presented to estimate the von Mises tension based on the information related to the cross-sectional area and stem curvature, and this model was able to estimate the maximum von Mises tension with an accuracy of 99%. Then the mentioned model was linked with the genetic algorithm and it was used to optimize the plow shaft. After selecting the optimized model through the genetic algorithm, the plow shaft was designed again and the tireless stress that occurred in it under the same loading conditions as the previous conditions was eliminated. The results showed that the amount of maximum stress in this model decreased by 6% compared to the previous models and the best stem designs is (Stress (MPa) VonMises=295.2).