Title :
Prediction of Material Mechanical Properties with Support Vector Machine
Author :
Tang, Jia-li ; Cai, Qiu-ru ; Liu, Yi-Jun
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;
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
DOI :
10.1109/MVHI.2010.58