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

Abstract:

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.

• Periodical: Applied Mechanics and Materials (Volume 318)
• Pages: 108-113
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.318.108
• Online since: May 2013
• Authors: Ji Yong Xu, Jun Li Zhao, Bing Zhao, Ying Qing Shao
• Keywords: Acoustic Emission Signal, BP Neural Network (BPNN), Drawing Parts, Location Identification

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