Abstract
This article introduces a step-by-step optimization method based on the radial basis function (RBF) surrogate model and proposes an improved expected improvement selection criterion to better the global performance of this optimization method. Then it is applied to the optimization of packing profile of injection molding process for obtaining best shrinkage evenness of molded part. The idea is first, to establish an approximation function relationship between shrinkage evenness and process parameters by a small size of design of experiment with RBF surrogate model to alleviate the expensive computational expense in the optimization iterations. And then, an improved criterion is used to provide direction in which additional training samples could be added to better the surrogate model. Two test functions are investigated and the results show that stronger global exploration performance and more precise optimal solution could be obtained with the improved method at the expense of increasing the infill data properly. Furthermore the optimal solution of packing profile is obtained for the first time which indicates that the type of optimal packing profile should be first constant and then ramp-down. Subsequently, the discussion of this result is given to explain why the optimal profile is like that.
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References
Rosato DV, Rosato DV, Rosato MG (2000) Injection molding handbook, 3rd edn. Kluwer, Boston
Chen X, Gao FR, Qian JX (2002) Research on quality model of packing stage: relationships between part quality and cavity pressure. China Plastics 16(4):51–54
Chen X, Gao FR (2003) A study of packing profile on injection molded part quality. Mater Sci Eng 358:205–213
Qiu B, Liu GH, Li W (2007) Effects of packing curve in injection molding on products surface quality. Engineering Plastics Application 35(1):37–39
Li HS, Guo ZY, Li DQ (2007) Reducing the effects of weldlines on appearance of plastic products by Taguchi experimental method. Int J Adv Manuf Technol 32:927–931
Kurtaran H, Erzurumlu T (2006) Efficient warpage optimization of thin shell plastic parts using response surface methodology and genetic algorithm. Int Adv Manuf Technol 27(5):468–472
Chen J, Savage M, Zhu JJ (2008) Development of artificial neural network-based in-process mixed-material-caused flash monitoring (ANN-IPMFM) system in injection molding. Int J Adv Manuf Technol 36:43–52
Gao YH, Wang XC (2007) Warpage optimization and influence factor analysis of injection molding. J Chem industry Ind 58(6):1576–1580
Mathivanan D, Parthasarathy NS (2009) Sink-mark minimization in injection molding through response surface regression modeling and genetic algorithm. Int Adv Manuf Technol (in press)
Gao YH, Wang XC (2008) An effective warpage optimization method in injection molding based on Kriging model. Int Adv Manuf Technol 37:953–960
Zhou J, Turng LS (2007) Process optimization of injection molding using an adaptive surrogate model with Gaussian process approach. Polym Eng Sci 47:684–694
Gao YH, Wang XC (2009) Surrogate-based process optimization for reducing warpage in injection molding. J Mater Process Technol 209:1302–1309
Sobester A, Leary SJ, Keane AJ (2005) On the design of optimization strategies based on global response surface approximation models. J Global Opt 33:31–59
Shie JR (2008) Optimization of injection molding process for contour distortions of polypropylene composite components by a radial basis neural network. Int J Adv Manuf Technol 36:1091–1103
Rasmussen CE, Williams Christopher KI (2006) Gaussian processes for machine learning. MIT Press, London
Jones DR, Schonlau M, Welch WJ (1998) Efficient global optimization of expensive black-box functions. J Global Opt 13:455–492
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Li, C., Wang, FL., Chang, YQ. et al. A modified global optimization method based on surrogate model and its application in packing profile optimization of injection molding process. Int J Adv Manuf Technol 48, 505–511 (2010). https://doi.org/10.1007/s00170-009-2302-6
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DOI: https://doi.org/10.1007/s00170-009-2302-6