Crack Position Recognition of Metal Deep Drawing Parts Based on BP Neural Network

被引:0
|
作者
Xu JiYong [1 ]
Zhao JunLi [1 ]
Zhao Bing [1 ]
Shao YingQing [1 ]
机构
[1] Jinshan Vocat & Tech Coll, Zhenjiang, Jiangsu, Peoples R China
来源
2013 INTERNATIONAL CONFERENCE ON MACHINERY, MATERIALS SCIENCE AND ENERGY ENGINEERING | 2013年 / 318卷
关键词
BP neural network; the drawing parts; acoustic emission signal; location identification;
D O I
10.4028/www.scientific.net/AMM.318.108
中图分类号
TE [石油、天然气工业]; TK [能源与动力工程];
学科分类号
0807 ; 0820 ;
摘要
Crack position of metal drawing parts molding was analyzed by the BP neural network. First analysis of the drawing parts forming process may crack in different position. The BP neural network location identification was introduced in the basic process. 11 characteristic parameters from the drawing parts may crack position were gathered by acoustic emission signal acquisition system of deep drawing process. Then the BP neural network was designed rational, and carried out appropriate conduct to train and test. Establishing deep drawing parts of the relations between the different positions crack acoustic emission characteristic parameters and the corresponding position. Crack location was identified, in order to achieve the purpose of positioning the work piece forming process. The better method of acoustic emission location issues are resolved, metal deep drawing forming of crack location identification for basis. Provide the basis for metal drawing parts forming crack location identification.
引用
收藏
页码:108 / 113
页数:6
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