Multi-objective process parameter optimization for energy saving in injection molding process

被引:0
|
作者
Ning-yun LU 1
机构
基金
中国国家自然科学基金;
关键词
Injection molding process; Energy saving; Multi-objective optimization; Genetic algorithm; Lexicographic method;
D O I
暂无
中图分类号
TQ320.66 [成型加工];
学科分类号
0805 ; 080502 ;
摘要
This paper deals with a multi-objective parameter optimization framework for energy saving in injection molding process.It combines an experimental design by Taguchi’s method,a process analysis by analysis of variance(ANOVA),a process modeling algorithm by artificial neural network(ANN),and a multi-objective parameter optimization algorithm by genetic algorithm(GA)-based lexicographic method.Local and global Pareto analyses show the trade-off between product quality and energy consumption.The implementation of the proposed framework can reduce the energy consumption significantly in laboratory scale tests,and at the same time,the product quality can meet the pre-determined requirements.
引用
收藏
页码:382 / 394
页数:13
相关论文
共 11 条
  • [1] A review on energy saving strategies in industrial sector[J] . E.A. Abdelaziz,R. Saidur,S. Mekhilef. Renewable and Sustainable Energy Reviews . 2010 (1)
  • [2] Quality control of the injection molding process using an EWMA predictor and minimum–variance controller
    Chin-Huang Sun
    Juhn-Horng Chen
    Long-Jye Sheu
    [J]. The International Journal of Advanced Manufacturing Technology, 2010, 48 : 63 - 70
  • [3] Reducing shrinkage in injection moldings via the Taguchi, ANOVA and neural network methods[J] . Mirigul Altan. Materials and Design . 2009 (1)
  • [4] An integrated parameter optimization system for MISO plastic injection molding
    Wen-Chin Chen
    Min-Wen Wang
    Chen-Tai Chen
    Gong-Loung Fu
    [J]. The International Journal of Advanced Manufacturing Technology, 2009, 44 : 501 - 511
  • [5] Optimization of injection molding process parameters using combination of artificial neural network and genetic algorithm method[J] . Changyu Shen,Lixia Wang,Qian Li. Journal of Materials Processing Tech. . 2006 (2)
  • [6] Comparison of the warpage optimization in the plastic injection molding using ANOVA, neural network model and genetic algorithm[J] . B. Ozcelik,T. Erzurumlu. Journal of Materials Processing Tech. . 2005 (3)
  • [7] Approximation methods in multiobjective programming
    Ruzika, S
    Wiecek, MM
    [J]. JOURNAL OF OPTIMIZATION THEORY AND APPLICATIONS, 2005, 126 (03) : 473 - 501
  • [8] Survey of multi-objective optimization methods for engineering
    Marler, RT
    Arora, JS
    [J]. STRUCTURAL AND MULTIDISCIPLINARY OPTIMIZATION, 2004, 26 (06) : 369 - 395
  • [9] A hybrid neural network and genetic algorithm approach to the determination of initial process parameters for injection moulding
    Mok, SL
    Kwong, CK
    Lau, WS
    [J]. INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY, 2001, 18 (06): : 404 - 409
  • [10] An analytical approach for determining the environmental impact of machining processes[J] . A.A. Munoz,P. Sheng. Journal of Materials Processing Tech. . 1995 (3)