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论文类型：期刊论文

发表时间：2015-06-01

发表刊物：INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY

收录刊物：SCIE、EI、Scopus

卷号：78

期号：9-12

页面范围：1813-1826

ISSN号：0268-3768

关键字：Injection molding; Kriging surrogate model; IEGO; Multi-objective optimization design; NSGA-II

摘要：This paper develops a framework that tackles the Pareto optimum of injection process parameters for multi-objective optimization of the quality of plastic part. The processing parameters such as injection time, melt temperature, packing time, packing pressure, cooling temperature, and cooling time are studied as model variables. The quality of plastic part is measured by warp, volumetric shrinkage, and sink marks, which is to be minimized. The two-stage optimization system is proposed in this study. In the first stage, an improved efficient global optimization (IEGO) algorithm is adopted to approximate the nonlinear relationship between processing parameters and the measures of the part quality. In the second stage, non-dominated sorting-based genetic algorithm II (NSGA-II) is used to find a much better spread of design solutions and better convergence near the true Pareto optimal front. A cover of liquid crystal display part is optimized to show the method. The results show that the Pareto fronts obtained by NSGA-II are distributed uniformly, and this algorithm has good convergence and robustness. The pair-wise Pareto frontiers show that there is a significant trade-off between warpage and volumetric shrinkage, and there is no significant trade-off between sink marks and volumetric shrinkage and between sink marks and warpage.