Advanced SearchSearch Tips
Prediction of Tensile Strength for Plasma-MIG Hybrid Welding Using Statistical Regression Model and Neural Network Algorithm
facebook(new window)  Pirnt(new window) E-mail(new window) Excel Download
  • Journal title : Journal of Welding and Joining
  • Volume 34, Issue 2,  2016, pp.67-72
  • Publisher : The Korean Welding and Joining Society
  • DOI : 10.5781/JWJ.2016.34.2.67
 Title & Authors
Prediction of Tensile Strength for Plasma-MIG Hybrid Welding Using Statistical Regression Model and Neural Network Algorithm
Jung, Jin Soo; Lee, Hee Keun; Park, Young Whan;
  PDF(new window)
Aluminum alloy is one of light weight material and it is used to make LNG tank and ship. However, in order to weld aluminum alloy high density heat source is needed. In this paper, I-butt welding of Al 5083 with 6mm thickness using Plasma-MIG welding was carried out. The experiment was performed to investigate the influence of plasma-MIG welding parameters such as plasma current, wire feeding rate, MIG-welding voltage and welding speed on the tensile strength of weld. In addition we suggested 3 strength estimation models which are second order polynomial regression model, multiple nonlinear regression model and neural network model. The estimation performance of 3 models was evaluated in terms of average error rate (AER) and their values were 0.125, 0.238, and 0.021 respectively. Neural network model which has training concept and reflects non -linearity was best estimation performance.
Al 5083 alloy;Plasma-MIG hybrid welding;Tensile strength estimation;Statistical regression model;Neural network model;
 Cited by
G. Mathers, The Welding of Aluminum and Its Alloys, Woodhead Publishing Ltd., 2002

C.H. Lee and R.W. Chang, Weldability of Al Alloys, Part I:Cracking and Porosity, Journal of the KWS, 10(3) (1992), 1-12 (in Korean)

H. Ton, Physical Properties of the Plasma-MIG Welding Arc, Appl Phys., 8(4) (1975), 922-933

W.G. Essers and R. Walter, Heat transfer and penetration mechanisms with GMA and plasma-GMA welding, Weld Journal, 60(2) (1981), 37-42

K. Ono, Z. Liu, T. Era, T. Uezono, T. ueyama, M. Tanaka and K. Nakata, Development of a Plasma MIG Welding System for Aluminum, Welding International, 23(11) (2009), 805-809 crossref(new window)

C.H. Kim, Y.N Ahn, J.K. Choi and S.H. Rhee, Process Evaluation of Plasma-GMA Welding for Al 5052 and 6061 Alloy, Journal of KWJS, 28(6) (2010), 58-62 (in Korean)

J.S. Jung, J.J. Lee, H.K. Lee and Y.W. Park, Weldability Evaluation in Plasma-GMA Hybrid Welding for Al-5083 Using Analysis of Variance, Journal of KWJS, 32(1) (2014), 28-33 (in Korean)

KS B ISO 4136:2008, Destructive tests on welds in metallic materials-Transverse tensile test, 2008

ANSI, AWS D1.2, Structural Welding Code-Aluminum (3th Edition), 1997

J.H. Cho, Prediction of Arc Welding Quality through Artificial Neural Network, Journal of KWJS, 31(3) (2013), 44-48 (in Korean)

J.H. Cho, Proper Arc Welding Condition Derivation of Auto-body Steel by Artificial Neural Network, J. of Welding and Joining, 32(2) (2014), 43-47 (in Korean)