期刊
CIRP JOURNAL OF MANUFACTURING SCIENCE AND TECHNOLOGY
卷 3, 期 1, 页码 66-72出版社
ELSEVIER SCIENCE BV
DOI: 10.1016/j.cirpj.2010.07.005
关键词
Support vector regression; Artificial neural network; Finite element simulation; Hydro-mechanical deep drawing
资金
- All India Council of Technical Education (AICTE)
In this paper, a new data mining technique support vector regression (SVR) is applied to predict the thickness along cup wall in hydro-mechanical deep drawing. After using the experimental results for training and testing, the model was applied to new data for prediction of thickness strains in hydro-mechanical deep drawing. The prediction results of SVR are compared with that of artificial neural network (ANN), finite element (FE) simulation and the experimental observations. The results are promising. It is found that SVR predicts the thickness variation in the drawn cups very accurately especially in the wall region. (C) 2010 CIRP.
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