tailieunhanh - The application of genetic algorithm to optimize technical parameters in profile grinding for ball bearing

The article presents a method to optimize technology parameters of the profile grinding operation for 6208 ball bearing's inner ring groove on the grinder 3MK136B. The research is implemented by the least squares experimental planning method to determine the experimental regression functions between technical parameters and output elements of the machining process. | Journal of Science & Technology 130 (2018) 028-032 The Application of Genetic Algorithm to Optimize Technical Parameters in Profile Grinding for Ball Bearing's Inner Ring Groove Nguyen Anh Tuan1*, Vu Toan Thang2, Nguyen Viet Tiep2 1 University of Economics and Technical Industries, No. 456 Minh Khai, Hai Ba Trung, Ha Noi, Viet Nam Hanoi University of Science and Technology, No. 1, Dai Co Viet Str., Hai Ba Trung, Ha Noi, Viet Nam Received: December 05, 2017; Accepted: November 26, 2018 2 Abstract In * the profile grinding operation for ball bearing's inner ring groove, the quality of products and the productivity of the machining process mostly depends on the technology system’s parameters such as normal feed rate (Fn), speed of part (Vp), depth of cutting (t), number of parts in a grinding cycle (Np), etc. It is actually necessary to optimize technology parameters of the machining process. The article presents a method to optimize technology parameters of the profile grinding operation for 6208 ball bearing's inner ring groove on the grinder 3MK136B. The research is implemented by the least squares experimental planning method to determine the experimental regression functions between technical parameters and output elements of the machining process. Based on that, an optimal solution of the non-linear optimization problem has been solved by using a Genetic Algorithm, presenting the most appropriate technology parameters for profile grinding of 6208 ball bearing’s inner ring groove on grinder 3MK136B as follows: Fn = (µm/s); Vp = (m/min); t = (µm) and Np = 19 (parts). Keywords: Genetic algorithm, Profile grinding, Cutting mode. facility and technology parameters. The optimization problem can be considered the problem of finding the best solution among an extremely large space of solutions. For small search space, traditional optimization methods can be suitable to solve (such as direct calculation method, graph method, Lagrange method, etc.), .