Influence of the Chemical Elements of Welding Materials on Mechanical Properties

Article Preview

Abstract:

Based on artificial neural network (ANN), a mechanical properties prediction model 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 mechanical properties, i.e. yield strength, tensile strength and elongation. 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. The influence of chemical compositions, such as C, S, P, Si, Mn, Cu, Ti and Ni on the mechanical properties of welding materials are analyzed. The results show that the influence of metallic elements is significantly greater than the nonmetallic. For nonmetallic elements, not all the value of mechanical properties decreases with the increase of the content. The influence of C is critical, followed by P and S. For metallic elements, the influence of different elements on mechanical properties, such as the yield strength, the tensile strength, the elongation and the average Charpy impact toughness, is difference.

You might also be interested in these eBooks

Info:

Periodical:

Advanced Materials Research (Volumes 562-564)

Pages:

719-722

Citation:

Online since:

August 2012

Export:

Price:

Permissions CCC:

Permissions PLS:

Сopyright:

© 2012 Trans Tech Publications Ltd. All Rights Reserved

Share:

Citation:

[1] S.S.M. Tavares , J.M. Pardal, and L.D. Lima: Mater. Charac. Vol. 58(2007), p.610.

Google Scholar

[2] K.Y. Benyounis, , and A.G. Olabi: Adv. Eng. Softw. Vol. 39(2008), p.483.

Google Scholar

[3] H. Ates. Mater. Design Vol. 28(2007), p. (2015).

Google Scholar

[4] H. Okuyucu, A. Kurt, and E. Arcaklioglu: Mater. Design Vol. 28(2007), p.78.

Google Scholar

[5] M. Vasudevan, B.P.C. Rao, and B. Venkatraman: J. Mater. Process Techno. Vol. 169(2005), p.396.

Google Scholar

[6] Z. Sterjovski , D. Nolan, and K.R. Carpenter: Mater. Process Techno. Vol. 170(2005), pp.536-44.

Google Scholar