Quality prediction and control of thin-walled shell injection molding based on GWO-PSO, ACO-BP, and NSGA-II

Abstract

ECG recorders are precision medical devices, but their thin-walled shells are susceptible to warpage and shrinkage during injection molding production due to the injection molding process, which greatly shortens their service life. To address this problem, a multiobjective optimization method for injection molding process parameters based on a combination of a BP neural network model optimized by an ant colony algorithm (ACO-BP) and an improved non-dominated sorting genetic algorithm (NSGA-II) is proposed. The study takes the warpage deformation amount and volume shrinkage rate of the plastic part as the optimization objectives, and the melt temperature, mold temperature, injection pressure, holding pressure, holding time, and cooling time as the design variables. However, for BP neural networks, it is crucial to choose an appropriate number of hidden layer neurons, so the particle swarm algorithm combined with the grey wolf algorithm (GWO-PSO) is used to solve for the optimal number of hidden layer neurons. Firstly, the number of hidden layer neurons of the BP network model was solved based on the samples obtained from the Box–Behnken experimental design and the GWO-PSO algorithm, and the ACO-BP algorithm was used to build the prediction models for warpage and volume shrinkage, respectively, and then combined with NSGA-II for global optimisation. The pareto optimal solution set was subjected to CRITIC analysis and the optimal process parameters were finally obtained, with a minimum warpage of 0.3293 mm and minimum volume shrinkage of 4.993%, a reduction of 8.93 and 6.95% respectively compared to the pre-optimisation period. At the same time, injection molding tests were carried out on the optimum process parameters, and it was found that the molding quality of the plastic parts was better and met the actual production requirements through measurement. The research in this paper provides a theoretical basis for further improving the quality defects of the thin-walled injection molded parts.

Research and Application of Improved Particle Swarm Fuzzy PID Algorithm Based on Self- Disturbance Rejection in Temperature Control System of Plastic Extruder

“…In the process of extruder barrel temperature control, this topic summarizes, organizes and analyzes the actual operating experience of the operators, and summarizes the specific experience as follows: in the heating stage, when │ e│is large, the difference between the actual temperature and the set target temperature is large [27], in order to make the temperature respond quickly, the kp value should be taken as a larger value; in order to prevent overshooting phenomenon caused by the saturation of integrals, the ki should be set to a smaller amount. In the transition process stage,│e│and │ec│size moderate, the actual temperature and the set target temperature difference is large, in order to reduce the amount of overshooting, Kp should be taken as the smaller value [28]; in order to ensure that the response rate of the system, should be taken as a moderate ki and kd value.…”