Open AccessMatching technology of reducer bearing based on genetic algorithm
Author(s) -
Yanzhong Wang,
Peng Liu,
Tao Ma,
Huihui Hao
Publication year - 2020
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1707/1/012026
Subject(s) - reducer , crossover , selection (genetic algorithm) , genetic algorithm , mathematical optimization , population , fitness function , coding (social sciences) , bearing (navigation) , computer science , algorithm , control theory (sociology) , engineering , mathematics , mechanical engineering , statistics , artificial intelligence , demography , control (management) , sociology
This paper presents a method of bearing selection for heavy reducer based on genetic algorithm. A mathematical model for the selection of key parts of bearing cap and adjusting ring is established, the coding mode of bearing cap and adjusting ring components is determined, the initial population is constructed, the fitness function is established, and the genetic calculations of selection, crossover and variation are carried out. Finally, through repeated experiments and 500 iterations, the value of the optimal objective function tends to be stable, and a good convergence effect is obtained. The assembly rate of the gearing cap-adjustment ring assembly is increased from the original 50% to over 70%, which greatly improves the assembly efficiency of heavy-duty reducer bearing, and thus the assembly cost is reduced, which is significant to the actual assembly of heavy-duty reducer.