Optimization of Injection Process Parameters for Automotive Instrument Framework Based on Particle Swarm Optimization Algorithm
laking a dashboard frame as an example,the warpage of plastic parts after injection molding was studied.Selecting mold temperature,melt temperature,cooling time,holding pressure,holding time,and injection time as independent variables,and warpage was treated as the optimization object.Through orthogonal experiments,process parameter combinations with smaller warpage within each process parameter range were obtained.BP neural network was used to establish a nonlinear model between the warpage of plastic parts and various process parameters.Using the BP neural network model as the fitness function of the particle swarm algorithm,the process parameters are optimized through the optimization ability of the particle swarm algorithm,and the minimum warpage of 0.936 3 mm and the optimal combination of process parameters are obtained,namely mold temperature 55.813 ℃,melt temperature 259.568 ℃,cooling time 29.650 s,holding pressure 85.02 MPa,holding time 29.187 s,and injection time 1.23 s.Using Moldflow mold flow analysis software,the optimal combination of process parameters is analyzed for warpage,and the simulated warpage is 0.948 9 mm.Comparing with the initial default values and the process parameter results are optimized by orthogonal experiments,the warpage is reduced by 64.48％and 19.17％,respectively,achieving the goal of optimizing the quality of plastic parts.
万方数据
维普
