Process parameters optimization of injection molding using a fast strip analysis as a surrogate mod.pdf

Process parameters optimization of injection molding using a fast strip analysis as a surrogate mod.pdf

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Process parameters optimization of injection molding using a fast strip analysis as a surrogate mod

ORIGINAL ARTICLE Process parameters optimization of injection molding using a fast strip analysis as a surrogate model Peng Zhao Huamin Zhou Yang Li Dequn Li Received: 6 May 2009 /Accepted: 9 November 2009 /Published online: 28 November 2009 # Springer-Verlag London Limited 2009 Abstract Injection molding process parameters such as injection temperature, mold temperature, and injection time have direct influence on the quality and cost of products. However, the optimization of these parameters is a complex and difficult task. In this paper, a novel surrogate-based evolutionary algorithm for process parame- ters optimization is proposed. Considering that most injection molded parts have a sheet like geometry, a fast strip analysis model is adopted as a surrogate model to approximate the time-consuming computer simulation software for predicating the filling characteristics of injection molding, in which the original part is represented by a rectangular strip, and a finite difference method is adopted to solve one dimensional flow in the strip. Having established the surrogate model, a particle swarm optimization algorithm is employed to find out the optimum process parameters over a space of all feasible process parameters. Case studies show that the proposed optimization algorithm can optimize the process parameters effectively. Keywords Injection molding . Parameters optimization . Surrogate model . Evolutionary algorithm . Fast strip analysis . Particle swarm optimization 1 Introduction Injection molding is the most widely used process for producing plastic products. During this process, many parameters such as injection temperature, mold tempera- ture, and injection time are very important, which have direct influence on the quality and cost of the products. However, the optimization of process parameters is a complex and difficult task [1–3]. An increase in injection temperature causes a decrease in melt viscosity, which results in reduced cavity pressure and

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