Abstract
The cooling system of a plastic injection mold plays a crucial role in determining both the productivity of the injection molding process and part quality. The cooling process is an important phase of injection molding, which could account for more than three-fourths of the total molding cycle time. Thus, an efficient cooling will significantly improve productivity by reducing the cooling time, and uniformity and balanced cooling can ensure part quality by preventing sink marks, different shrinkage, internal thermal residual stress and warpage.
In this paper, the geometric parameters (i. e., radius and location of each cooling channel) and process parameters (i. e., inlet coolant temperature, volumetric flow rate of each cooling channel, packing time, and cooling time, etc.) are considered systematically in the cooling system design of injection molding. A two-level decomposition method for the cooling system optimization is proposed. The optimization problem is divided into two subproblems (cooling channel design and process design) on the basis of the different effects on refrigerant behaviour of different kinds of design variables. By combining the Kriging model and CAE technology, a mold cooling system for a 15-inch display was optimized. The results show that the method can efficiently improve the temperature distribution and the part quality.
References
Gao, Y. H., Wang, X. C., “An Effective Warpage Optimization Method in Injection Molding Based on the Kriging Model”, Int. J. Adv. Manuf. Technol., online (2007)10.1007/s00170-007-1044-6Search in Google Scholar
Hawe, G. I., Sykulski, J. K., “A Hybrid One-then-two Stage Algorithm for Computationally Expensive Electromagnetic Design Optimization”, Optimization and Inverse Problems in Electromagnetics, 191–192 (2006)Search in Google Scholar
Huang, D., et al., “Sequential Kriging Optimization Using Multiple-fidelity Evaluations”, Struct. Multidisc. Optim., 32, 369–382 (2006)10.1007/s00158-005-0587-0Search in Google Scholar
Jones, D. R., et al., “Efficient Global Optimization of Expensive Black-box Functions”, J. Global Optimization, 13, 455–492 (1998)10.1023/A:1008306431147Search in Google Scholar
Lam, Y. C., et al., “An Evolutionary Approach for Cooling System Optimization in Plastic Injection Moulding”, Int. J. Prod. Res., 42, 2047–2061 (2004)10.1080/00207540310001622412Search in Google Scholar
Lee, S. H., et al., “Cylindrical Tube Optimization Using Response Surface Method Based on Stochastic Process”, J. Mater. Process. Technol., 130-131, 490–496 (2002)10.1016/S0924-0136(02)00794-XSearch in Google Scholar
Lin, J. C., Optimum cooling system design of a free-form injection mold using an abductive network, J. Mater. Process. Technol., 120, 226–236 (2002)10.1016/S0924-0136(01)01193-1Search in Google Scholar
Lophaven, S. N., et al, “DACE-A Matlab Kriging Toolbox”, Version 2. Informatics and Mathematical Modeling. Technical University of Denmark (2002)Search in Google Scholar
Lophaven, S. N., et al., “Aspects of the Matlab Toolbox DACE, Technical Report IMM-REP-2002-13, Informatics and Mathematical Modeling [DB/OL]. Technical University of Denmark (2002)Search in Google Scholar
Matsumoto, T., Tanaka, M., “Optimum design of cooling lines in injection moulds by using boundary element design sensitivity analysis”. Finite Elements in Analysis and Design.14, 177–185 (1993)10.1016/0168-874X(93)90018-LSearch in Google Scholar
McKay, M. D., et al., “A Comparison of Three Methods for Selecting Value of Input Variables in the Analysis of Output form a Computer Code”, Technometrics, 21, 239–245 (1979)Search in Google Scholar
Park, S. J., Kwon, T. H., “Optimal Cooling System Design for the Injection Molding Process”, Polym Eng. Sci., 38, 1450–1462 (1998a)10.1002/pen.10316Search in Google Scholar
Park, S. J., Kwon, T. H., “Thermal and Design Sensitivity Analysis for Cooling System of Injection Mold, Part 2: Design Sensitivity Analysis”, J. Manuf. Sci. Eng., 120, 296–305 (1998b)10.1115/1.2830127Search in Google Scholar
Rezayat, M., Burton, T. E., “A Boundary-integral Formulation for Complex Three-dimensional Geometries”, International Journal for Numerical Methods in Engineering, 29, 263–273 (1990)10.1002/nme.1620290204Search in Google Scholar
Simpson, T. W., et al., “Comparison of Response Surface and Kriging Models for Multidisciplinary Design Optimization”, AIAA Meeting Papers, 1–11 (1998)10.2514/6.1998-4755Search in Google Scholar
Tang, L. Q., et al., “Optimal Cooling System Design for Multi-cavity Injection Molding”, J. Finite Elements in Analysis and Design, 26, 229–251 (1997)10.1016/S0168-874X(96)00083-2Search in Google Scholar
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