Integrated Optimization Methods On Multi-varity And Multi-objective Of Multi-cavity Slender Rod Injection Molding Process

The quality inconsistency of multi-cavity pen rod parts is the technical weakness for the pen companies to overcome. Based on the balanced arrangement, this paper designed a kind of one-mold-four-cavity ballpoint pen rod mold, which referred research methods melt filling flow theory in the traditional of the injection molding, introduced reasonable assumptions and necessary simplifications, and constructed the mathematical model of melt filling flow in the slender pen rod injection molding. Various factors of the unbalance filling in the runner in the balanced design were analyzed, especially the mechanism of shear-induced imbalance in the balanced flow channels. In the numerical simulation, Y-direction maximum deformation was determined as the evaluation objective of mobile unbalance. Through changing the process parameters such as mold temperature, melt temperature, injection time, holding pressure and so on, the flow filling and Y-direction maximum deformation reached the acceptable balance. On this basis, the theoretical analysis and design optimization results were experimentally proposed.It is determined two conflicting objectives on the quality of related product—the maximum volume shrinkage(R1) and the maximum axial deformation(R2) as the optimization objects, and the difference of normal orthogonal design and the Taguchi design based on the noise ratio was compared to seek the optimal process parameter combination and factor significance through the range and variance analysis. The experiment can show that Taguchi method with robust technology was better than the common orthogonal experiment at the single-objective problem no matter from AVONA analysis significance or the process parameter optimal level combination. After using the intuitive analysis, the conflicting bi-objective still preferred to use the Taguchi method with robust technology.Concerning the analysis on the interaction between the accuracy and factors, it was proposed to design the response surface (RSM) regression model based on Central composite design (CCD) of slender pen rod, the non-linear relationship was established between the design variable and design response, the optimal process parameter combination in the continuous space was sought, and the bi-objective optimization based on weighing was conducted. Compared with the orthogonal experiment based on the noise ratio, the accuracy of RSM was higher. With the inspection on the unbalance analysis of the Y-direction maximum deformation, the filling balance of the optimal process parameter based on RSM was better.A kind of integrated optimization design method based on the genetic algorithm and neural network was developed. GA was used to optimize the weight and threshold of BP neural network:Each individual in the population contains a BP network ownership value and threshold, and the individual calculates the fitness value through the fitness function. The individual corresponding optimal fitness value of BP network was found by GA algorithm selected, crossed and mutated. The network initial weight and threshold on the best individual assigned BP were obtained with GA. After training, the BP network would predict the function output. The optimization result can show that the optimized design method integrated by the genetic algorithm and neural network (GA-BP) has a fast convergence speed, higher approaching degree on the global optimal solution. It can quickly offer the process parameter combination of expected plastic part value. With the trained network, the influence rules of mold temperature, melt temperature, holding time and holding pressure and other process parameters on^l and^2were obtained when the multi-cavity mold slender rod was selected different materials.Finally, aiming at the multi-objective feature commonly existed in the injection molding and optimization, the multi-cavity slender pen rod injection molding multi-objective optimization method based on the analysis of gray correlation analysis (GCA) was firstly proposed. Concerning the objective functions of target-the-better, larger-the-better and smaller-the-better features, different target data preprocessing methods were proposed. In order to obtain the best optimal parameter combination, it conducted the variance analysis on the multi-objective gray correlation degree, established the response function significantly influencing the variable, and obtained the best value. The improved traditional research result can show that conducting the multi-objective optimization with the improved optimized measures on the injection process multi-objective optimization by use of the gray correlation analysis and ideal solution can obtain the best result.After simulation and verification of enterprises experimental, the results were consistent with the actual production.