Injection Processing Parameters Forecast for Thin Wall Plastics Parts Based on Response Surface Methodology and Genetic Algorithm#br#

Abstract

Abstract:

This article emploved the response surface methodology( RSM), combined with CAE tech-noloeyand analysis of varance theory (ANOVA) to establsh an ontimization model. which describes the relationship betweenthe mainprocessing parameters and thin wall part quality. The processing pa- rameters were optimized using the genetic algorithm (GA) A case study showed that the method was effective and feasible.

Key words: response surface methodolog57, genetic algorithm, thin wall plastics part, processing parameter, optimization

CLC Number:

TQ320 a 66十2

LIU Dong-lei, SUN Ling, XIN Yong. Injection Processing Parameters Forecast for Thin Wall Plastics Parts Based on Response Surface Methodology and Genetic Algorithm#br#[J]. China Plastics, 2007, 21(3): 53-57.

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Injection Processing Parameters Forecast for Thin Wall Plastics Parts Based on Response Surface Methodology and Genetic Algorithm#br#

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