Multi-objective optimization method for injection molding parameters

Abstract

The invention discloses a multi-objective optimization method for injection molding parameters. The method is a fuzzy decision Gkriging-NSGA-value strategy processing multi-objective optimization design method based on an improved Kriging agent model Gkriging, a non-dominated sorting genetic algorithm NSGA-II and a value set, and is used for solving the problem of fuzzy decision Gkriging-NSGA-value strategy processing multi-objective optimization design. Selecting injection mold sub-runner section size parameters and injection molding process parameters as decision variables to be optimized; respectively taking the maximum volume shrinkage rate, the flow channel total volume and the forming period of the product as evaluation indexes of the product quality, the production cost and the production efficiency, establishing a model, obtaining an optimal value of multiple targets of the quality, and realizing multi-target optimization of the comprehensive quality of the product through a multi-target decision-making method. According to the injection molding parameter multi-objective optimization method, high-quality injection molding products can be economically and rapidly obtained onthe basis of a one-mold two-piece mold.

Description

technical field [0001] The invention relates to a parameter multi-objective optimization method, in particular to a Gkriging-NSGA-vague strategy-based injection molding parameter multi-objective optimization method, which belongs to the technical field of injection molding processing. Background technique [0002] Injection molding is a multi-parameter, strongly coupled, complex nonlinear system whose input is the performance parameters of polymer materials, mold design parameters, and molding process parameters, and whose output is the utilization rate of polymer materials, injection molding efficiency, and molded product quality. evolutionary process. Since the one-piece two-piece mold can make full use of the mold space and reduce energy consumption and processing costs, the one-piece two-piece mold is widely used in the injection molding industry. However, due to the unnatural equilibrium arrangement of a two-piece mold, the flow distance of the polymer melt in each cav...

AI Technical Summary

Problems solved by technology

However, the key to the construction of the Kriging model is the determination of the relevant model parameters. Most of the existing research uses the maximum likelihood estimation method to determine the relevant model parameters, which makes the selection of initial values ​​very difficult.

Many scholars select the initial value multiple times within the range of relevant model parameters. This method of value selection is a complex and time-consuming work, and it is impossible to obtain the optimal relevant model parameters.

In addition, according to the above research and analysis, most researchers only regard the flow channel cross-sectional size parameters or injection molding process parameters as design variables, but do not consider that only when the two are optimal at the same time can the expected molding results be obtained.

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