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Development of empirical models for prediction of weld bead geometry in robotic - GMAW

Wybrane pełne teksty z tego czasopisma
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Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
Purpose: The objective of this paper is to determine the input-output relationship of robotic gas metal arc welding process using linear as well as second order linear regression analysis. Design/methodology/approach: Taguchi’s L27, 3 level 4 parameter orthogonal array design of experiments and multiple regression techniques has been utilized for the development of empirical model. Arc current, stick-out, arc voltage and travel speed is taken as input parameters and bead geometry has been taken as output responses. The effects and interaction terms on different responses of these selected welding parameters have been analyzed using ANOVA. Findings: Both techniques results were compared and concluding remarks have been made. The developed empirical model has been found good agreement with the experiment results and predicted error for second order polynomial regression equations lies between 0.58% to 14.86% for bead height, 0.93% to 9.44% for bead width and 0.34% to 2.56% for bead penetration using with actual experimental results. Research limitations/implications: It was noticed that interaction effects have considerable influence on the formation of weld bead geometry, so it cannot be ignored. Originality/value: In this present work, an effort has been made to carry out both first as well as second order linear regression analyses on robotic GMAW by L27, Taguchi’s design of experiments.
Rocznik
Strony
72--85
Opis fizyczny
Bibliogr. 20 poz., rys., tab.
Twórcy
  • Faculty in the Department of Mechanical Engineering, Sona College of Technology, Salem-5, Tamilnadu, India
  • Department of Mechanical Engineering, Jayalakshmi Institute of Technology, Thoppur, Dharmapuri, Tamilnadu, India
  • Faculty in the Department of Mechanical Engineering, Sona College of Technology, Salem-5, Tamilnadu, India
Bibliografia
  • [1] K.Y. Benyounis, A.G. Olabi, M.S.J. Hashmi, Effect of laser welding parameters on the heat input and weld-bead profile, Journal of Materials Processing Technology 164-165 (2005) 978-985.
  • [2] K.Y. Benyounis, A.G. Olabi, M.S.J. Hashmi, Optimizing the laser-welded butt joint of medium carbon steel using RSM, Journal of Materials Processing Technology 164-165 (2005) 986-989.
  • [3] V.K. Gupta, R.S. Parmar, Fractional factorial technique to predict dimensions of the weld bead in automatic submerged arc welding, IE(I) Journal of Metal Construction 70 (1989) 67-75.
  • [4] V. Gunaraj, N. Murugan, Application of response surface methodology for predicting weld bead quality in submerged arc welding of pipes, Journal of Materials Processing Technology 88 (1999) 266-275.
  • [5] V. Gunaraj, N. Murugan, Prediction and comparison of the area of the heat-affected zone for the bead-on-plates and bead-on-joint in submerged arc welding of pipes, Journal of Materials Processing Technology 95 (1999) 246-261.
  • [6] V. Gunaraj, N. Murugan, Prediction and optimization of weld bead volume for the submerged arc process – part 1,Welding Journal AWS October (2000) 286-s–294-s.
  • [7] I.S. Kim, J.S. Son, I.G. Kim, O.S. Kim, A study on relationship between process variable and bead penetration for robotic CO2 arc welding, Journal of Materials Processing Technology 136 (2003) 139-145.
  • [8] I.S. Kim, Y.J. Jeong, K.J. Son, K.J. Kim, J.Y. Kim, I.K. Kim, P.K.D.V. Yaragada, Sensitivity analysis for process parameters influencing weld quality in robotic GMA welding processes, Journal of Materials Processing Technology 140 (2003) 676-681.
  • [9] I.S. Kim, K.J. Son, Y.S. Yang, P.K.D.V. Yaragada, Sensitivity analysis for process parameters in GMA welding processes using a factorial design method, International Journal of Machine Tool Manufacturing 43 (2003) 763-769.
  • [10] E. Koleva, Statistical modeling and computer programs for optimization of the electron beam welding of stainless steel, Journal of Vacuum 62 (2001) 151-157.
  • [11] E. Koleva, I. Vuchkov, Model-based approach for quality improvement of electro beam welding applications in mass production, Journal of Vacuum 77 (2005) 423-428.
  • [12] K. Manonmani, N. Murugan, G. Buvanasekaran, Effect of process parameters on the weld bead geometry of laser beam welded stainless steel sheets, International Journal of Joining of Materials 17/4 (2005) 103-109.
  • [13] N. Murugan, R.S. Parmar, Effects of MIG process parameters on the geometry of the bead in the automatic surfacing of stainless steel, Journal of Materials Processing Technology 41 (1994) 381-398.
  • [14] N. Murugan, R.S. Parmar, S.K. Sud, Effect of submerged arc welding process variables on dilution and bead geometry in single wire surfacing, Journal of Materials Processing Technology 37 (1993) 767-780.
  • [15] J.N. Pires, A. Loureiro, G. Bölmsjo, Welding Robots - Technology, System Issues and Applications, Springer-Verlag, London, 2006.
  • [16] J. Raveendra, R.S. Parmar, Mathematical models to predict weld bead geometry for flux cored arc welding, Journal of Metal Construction 19/2 (1987) 31R-35R.
  • [17] C.M.D. Starling, P.V. Marques, P.J. Modenesi, Statistical modeling of narrow-gap GTA welding with magnetic arc oscillation, Journal of Materials Processing Technology 51 (1995) 37-49.
  • [18] P. Thamilarasi, S. Raghunathan, E. Mohankumar, Optimization of process parameters of robotic GMAW of IS2062 E250BR using Taguchi techniques, International Review of Mechanical Engineering 8/2 (2014) 302-308.
  • [19] C. Weisman (Ed.), Fundamentals of Welding, Welding handbook, Vol. 1, American Welding Society, Florida, 1976.
  • [20] L.J. Yang, M.J. Bibby, R.S. Chandel, Linear regression equations for modeling the submerged-arc welding process, Journal of Materials Processing Technology 39 (1993) 33-42.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-a88d6e3b-050a-4daa-825c-be4f4aaff161
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