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Predicting Lap-Joint bead geometry in GMA welding process

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Wybrane pełne teksty z tego czasopisma
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Języki publikacji
EN
Abstrakty
EN
Purpose: The prediction of the optimal bead geometry is an important aspect in robotic welding process. Therefore, the mathematical models that predict and control the bead geometry require to be developed. This paper focuses on investigation of the development of the simple and accuracy interaction model for prediction of bead geometry for lap joint in robotic Gas Metal Arc (GMA) welding process. Design/methodology/approach: The sequent experiment based on full factorial design has been conducted with two levels of five process parameters to obtain bead geometry using a GMA welding process. The analysis of variance (ANOVA) has efficiently been used for identifying the significance of main and interaction effects of process parameters. General linear model and regression analysis in SPSS has been employed as a guide to achieve the linear, curvilinear and interaction models. The fitting and the prediction of bead geometry given by these models were also carried out. Graphic results display the effects of process parameter and interaction effects on bead geometry. Findings: The fitting and the prediction capabilities of interaction models are reliable than the linear and curvilinear models. It was found that welding voltage, arc current, welding speed and 2-way interaction CTWD×welding angle have the large significant effects on bead geometry. Practical implications: The model should also cover a wide range of material thicknesses and be applicable for all welding position. For the automatic welding system, the data must be available in the form of mathematical equations. Originality/value: It has been realized that with the use of the developed algorithm, the prediction of optimal bead dimensions becomes much simpler to even a novice user who has no prior knowledge of the robotic GMA welding process and optimization techniques.
Rocznik
Strony
121--124
Opis fizyczny
Bibliogr. 9 poz.
Twórcy
autor
autor
autor
autor
  • Department of Mechanical Engineering, Mokpo National University, 16, Dorim-ri, Chungkye-myun, Muan-gun, Jeonnam, 534-729, Korea, ilsookim@mokpo.ac.kr
Bibliografia
  • [1] D. Kim, M. Kang, S. Rhee, Determination of optimal welding conditions with a controlled random search procedure, Welding Journal 8 (2005) 125-130.
  • [2] J. Raveendra, R.S. Parmar, Mathematical models to predict weld bead geometry for flux cored arc welding, Metal Construction 19/2 (1987) 31-35.
  • [3] L.J. Yang, R.S. Chandel, M.J. Bibby, The effects of process variables on the weld deposit area of submerged arc welds, Welding Journal 72/1 (1993) 11-18.
  • [4] S. Datta, M. Sundar, A. Bandyopadhyay, P.K. Pal, S.C. Roy, G. Nandi, Statistical modeling for predicting bead volume of submerged arc butt welds, Australasian Welding Journal 51 (2006) 4-8.
  • [5] V. Gunaraj, N. Murugan, Prediction and optimization of weld bead volume for the Submerged Arc Process - Part 1, Welding Journal 10 (2000) 286-294.
  • [6] V. Gunaraj, N. Murugan, Prediction and optimization of weld bead volume for the Submerged Arc Process - Part 2, Welding Journal 11 (2000) 331-338.
  • [7] I.S. Kim, J.S. Son, C.E. Park, I.J. Kim, H.H. Kim, An investigation into an intelligent system for predicting bead geometry in GMA welding process, Journal of Materials Processing Technology 159/1 (2005) 113-118.
  • [8] P. Li, M.T.C. Fang, J. Lucas, Modelling of submerged arc welding bead using self-adaptive offset neural network, Journal of Materials Processing Technology 71 (1997) 228-98.
  • [9] Y.S. Tang, H.L. Tsai, S.S. Yeh, Modelling, optimization and classification of weld quality in tungsten inert gas welding, Journal of Machine Tools Manufacture 39 (1999) 1427-38.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-article-BSL8-0028-0051
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