On the algorithms of design optimization of crankshaft bearing based on multi-objective of system

Currently only the bearing was usually taken as the studying object in the optimization of crankshaft bearing. However, there is the corresponding relationship between the main dimensions (the diameter and width of journal) of the crankshaft bearing and crankshaft in an internal combustion engine, and there is the interaction between the crankshaft bearing and crankshaft in operation. In this paper, the crankshaft-bearing system of a four-cylinder engine was taken as the studying object, and an integrated optimization design of crankshaft bearing based on the multi-object was carried out. The total average frictional power loss of crankshaft bearings and the crankshaft mass were chosen as the objective functions, and the algorithms of the Particle Swarm Optimization (PSO) with the idea of decreasing inertia weight based on exponential curve strategy and the simulated annealing (SA) were used in the intelligent optimization. The results of comparing the two algorithms with the ones of original design show that, 26.2% and 25.8% reduction in total average frictional power loss of crankshaft beatings and 5.3% reduction of the crankshaft mass were presented by the optimization design, which are more reasonable than the original design. By comparison, the PSO algorithm has higher precision than the SA algorithm, but the calculation time of the SA algorithm is less than that of the PSO algorithm.