Application of Genetic Algorithms in Automated Mechanical Design

The application of automated assembly equipment plays an important role in the application of intelligent automated machinery design, but there is a problem of inaccurate application positioning. Traditional deep learning cannot solve the mechanical design problems in the application of intelligent automated mechanical design, and the effect is not ideal. Therefore, this paper proposes the application of automated machinery design based on genetic algorithm and analyzes the application in automated machinery design. Firstly, the influencing factors is located by natural selection theory, and the indicators is divided according to the requirements of the application of automated assembly equipment to reduce the interference factors in the application of automated assembly equipment. Then, the theory of natural selection is used to form the application scheme of genetic algorithm automatic assembly equipment, and the application results of automatic assembly equipment is comprehensively analyzed. The MATLAB simulation results show that under certain evaluation criteria, the genetic algorithm is superior to traditional deep learning in terms of application accuracy of automated assembly equipment and time of influencing factors of automated assembly equipment.