Prediction Model of the Charpy Impact Toughness of Deposited Metals of Welding Materials

Based on artificial neural network (ANN), prediction model of the Charpy impact toughness for automatic welding is built. The input parameters of the model consist of the chemical elements and the diameter of the welding material and the outputs is the average Charpy impact toughness. The ANNs model...

Full description

Saved in:
Bibliographic Details
Published inApplied Mechanics and Materials Vol. 130-134; pp. 1001 - 1004
Main Authors Sui, Yong Li, Bai, Lu, Tong, Li Ge, Ding, Hong Sheng, Wang, Li, Bai, Shi Wu, Yu, Jiang Feng
Format Journal Article
LanguageEnglish
Published Zurich Trans Tech Publications Ltd 01.01.2012
Subjects
Welding
Selection of Components
Average Charpy Impact Toughness
Online AccessGet full text

Cover

More Information
Summary:Based on artificial neural network (ANN), prediction model of the Charpy impact toughness for automatic welding is built. The input parameters of the model consist of the chemical elements and the diameter of the welding material and the outputs is the average Charpy impact toughness. The ANNs model is established by Visual C++ based on improved back-propagation (BP) arithmetic with momentum coefficients, in which the sample data used are from automatic welding materials for X70 pipeline steel. Based on the prediction model, the influence of chemical compositions, such as C, S, P, Si, Mn, Cu, Ti and Ni on the Charpy impact toughness of welding materials are analyzed. The results show that the influence of metallic elements is significantly greater than the nonmetallic, and the contents of Mn in metallic and the C in nonmetallic have primary effect on the average Charpy impact toughness.
Bibliography:Selected, peer reviewed papers from the 2011 3rd International Conference on Mechanical and Electronics Engineering (ICMEE 2011), September 23-25, 2011, Hefei, China
ISBN:3037852860
9783037852866
ISSN:1660-9336
1662-7482
1662-7482
DOI:10.4028/www.scientific.net/AMM.130-134.1001