• DocumentCode
    3305049
  • Title

    Prediction of Material Mechanical Properties with Support Vector Machine

  • Author

    Tang, Jia-li ; Cai, Qiu-ru ; Liu, Yi-Jun

  • fYear
    2010
  • fDate
    24-25 April 2010
  • Firstpage
    592
  • Lastpage
    595
  • Abstract
    Predicting model which refers to mechanical properties with material composition and techniques can be founded to reduce test times, increase efficiency and realize the optimization of process. This paper proposes to apply the Support Vector Machine to set up the nonlinear mapping from influence factors of material performances to mechanical properties. Taking the wheat straw-reinforced composite for instance, the prediction model based on support vector machine has been built. Besides, the model is used to optimize process parameters of injection molding and find the range of best parameters. The simulation result shows the founded model has preferable learning and generalization capabilities, which performs effectively in predicting mechanical properties. Therefore it is feasible to optimize process parameters and the technology is worthy to be applied and spread in the research of material performance.
  • Keywords
    Artificial neural networks; Composite materials; Injection molding; Kernel; Machine learning; Machine learning algorithms; Materials testing; Mechanical factors; Predictive models; Support vector machines; Support Vector Machine; material mechanical properties; predicting model;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Machine Vision and Human-Machine Interface (MVHI), 2010 International Conference on
  • Conference_Location
    Kaifeng, China
  • Print_ISBN
    978-1-4244-6595-8
  • Electronic_ISBN
    978-1-4244-6596-5
  • Type

    conf

  • DOI
    10.1109/MVHI.2010.58
  • Filename
    5532566