基于神经网络与PSO算法的发动机装配工艺参数优化

针对汽车发动机装配过程中缸体泄漏问题,结合Back Propagation(BP)神经网络及粒子群优化(Particle Swarm Op-timization,PSO)算法,提出了一种发动机装配工艺参数优化方法.首先,使用BP神经网络建立了生产工艺参数与质量指标之间的非线性映射关系,并以此作为泄漏率预测模型.其次,根...

Optimization of engine assembly process parameters based on neural network and PSO algorithm

Aiming at the problem of cylinder block leakage in the assembly process of automobile engine,combined with Back Propagation(BP)neural network and Particle Swarm Optimization(PSO)algorithm,an optimization method of engine assembly process parameters was proposed.Firstly,the BP neural network was used to establish the non-linear mapping relationship between the production process parameters and the quality indicators,which was used as the leakage rate prediction model.Then according to the actual production needs,the Pearson correlation analysis was used to obtain the process parameters of the most relevant part of the station as the subsequent optimization objects.Finally,the BP neural network prediction model was used as the fitness function,and the PSO algorithm was used to obtain the optimal value of the process parameters.The actual production data of 400 engines was used for experiments.The experimental results show that the BP neural network has a more accurate prediction effect.Combined with the PSO algorithm,the optimized process parameters were obtained,which significantly reduced the engine leakage rate and has certain guiding significance.