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Research on optimization design of the heating/cooling channels for rapid heat cycle molding based on response surface methodology and constrained particle swarm optimization
Authors:Guilong Wang  Guoqun Zhao  Huiping Li  Yanjin Guan
Affiliation:1. Kanazawa University, Kakuma-machi, Kanazawa 920-1192, Japan;2. Graduate School of Natural Science & Technology, Kanazawa University, Kakuma-machi, Kanazawa 920-1192, Japan;3. Industrial Research Institute of Ishikawa, 2-1, Kanazawa, Kuratsuki 920-8203, Japan;4. Sodick Co., Ltd., Ka-1-1, Miya-machi, Kaga-shi, Ishikawa 922-0595, Japan;1. Department of Mechanical Engineering, University of South China, Hengyang 421001, PR China;2. Lab for Micro Molding and Polymer Rheology, South China University of Technology, Guangzhou 510640, PR China
Abstract: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.
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