Research on optimization design of the heating/cooling channels for rapid heat cycle molding based on response surface methodology and constrained particle swarm optimization

被引:63
作者
Wang, Guilong [1 ]
Zhao, Guoqun
Li, Huiping
Guan, Yanjin
机构
[1] Shandong Univ, Engn Res Ctr Mold & Die Technol, Jinan 250061, Shandong, Peoples R China
关键词
Injection molding; Rapid heat cycle molding (RHCM); Steam heating; Response surface methodology (RSM); Particle swarm optimization (PSO); CONFORMAL COOLING CHANNELS; SYSTEM-DESIGN; TEMPERATURE;
D O I
10.1016/j.eswa.2010.11.063
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
The aim of this work is to optimize the layout of the heating/cooling channels for rapid heat cycle molding with hot medium heating and coolant cooling by using response surface methodology and optimization technique. By means of a Box-Behnken experiment design technique, an experiment matrix with three factors and three levels was designed. The design variables including the diameter of the heating/cooling channels, distances from the wall of heating/cooling channel to the cavity surface and between the adjacent heating/cooling channels were used to describe the layout and shape of the heating/cooling channels. The heating efficiency, standard deviation of the cavity surface temperature and the maximum von-mises stress were considered as the model variables. Thermal response and structural strength analyses of the mold based on FEM were conducted to acquire the objective variables for combination of process parameters. Some mathematical models of response surface were created by the mixed regression model and response surface method. The analysis of variance (ANOVA) method was used to check the accuracy of the developed mathematical models. With these mathematical models, the layout of the heating/cooling channels was then optimized to minimize the required heating time within reasonable temperature distribution and structural strength of the cavity by coupling the developed response surface (RS) models with the particle swarm optimization (PSO) method. (C) 2010 Elsevier Ltd. All rights reserved.
引用
收藏
页码:6705 / 6719
页数:15
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