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Development of the intelligent algorithm to control on-line bead height for robotic welding process

Wybrane pełne teksty z tego czasopisma
Identyfikatory
Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
Purpose: The demand to increase productivity and quality, the shortage of skilled labour and strict health and safety requirements finally led to the development of the robotic welding process to deal with many problems of the welded fabrication. Many techniques were developed to control process parameters to get the optimal bead geometry during welding process by minimizes their magnitude in the affected area. Design/methodology/approach: The development of thermo mechanical mechanism in some techniques is not fully understood. To solve this problem, we have carried out the sequential experiment based on a Taguchi method and identified the various problems that result from the robotic GMA welding process. Findings: To characterize the GMA welding process and establish guidelines for the most effective joint design. Also using multiple regression analysis with the help of a standard statistical package program, SPSS, on an IBM-compatible PC, a quadratic model has been developed for on-line control which studies the influence of process parameters on bead height and compares their influences on the bead height to see which one of process parameters is most affecting. Originality/value: This model developed has been employed the prediction of optimal process parameters and assisted in the generation of process control algorithms.
Rocznik
Strony
87--93
Opis fizyczny
Bibliogr. 14 poz., rys., tab.
Twórcy
autor
  • Department of Mechanical Engineering, Mokpo National University, 61, Dorim-ri, Chungkye-myun, Muan-gun, Jeonnam, 534-729, Republic of Korea
autor
  • Department of Mechanical Engineering, Mokpo National University, 61, Dorim-ri, Chungkye-myun, Muan-gun, Jeonnam, 534-729, Republic of Korea
autor
  • Department of Mechanical Engineering, Mokpo National University, 61, Dorim-ri, Chungkye-myun, Muan-gun, Jeonnam, 534-729, Republic of Korea
autor
  • Department of Mechanical Engineering, Mokpo National University, 61, Dorim-ri, Chungkye-myun, Muan-gun, Jeonnam, 534-729, Republic of Korea
autor
  • Department of Mechanical System, Daejeon Campus of Korea Polytechnic Colleges, 352-21, Uam-ro, Dong-gu, Daejeon, 300-702, Republic of Korea
Bibliografia
  • [1] R.S. Chandel, S.R. Bala, Effect of welding parameters and groove angle on the soundness of root beads deposited by the SAW process, Proceedings of the International Conference on “Trends in Welding Research”, Gatlinburg, Tennessee, 1986, 479-385.
  • [2] D.K. Feder, Computers in welding technology, A look at applications and potentials, Welding quality, the Role of Computers, IIW, Vienna, 1988, 17-35.
  • [3] G.E. Cook, K. Andersen, R. J. Barrett, Feedback and adaptive control in welding, Proceedings of the 2nd International Conference on “Trends in Welding Research”, 1988, 891-903.
  • [4] J.A. Freeman, D.M. Shapura, Neural Networks Algorithms, Applications and Programming Techniques, Addison-Wesley, 1991.
  • [5] L. Burke, J.P. Ignizio, A practical overview of neural networks, Journal of Intelligent Manufacturing 8 (1997) 157-165.
  • [6] G.E. Cook, Feedback and adaptive control in automated arc welding system, Metal Construction 13/9 (1981) 551-556.
  • [7] S.C. Juang, Y.S. Tarng, H.R. Li, A comparison between the back-propagation and counter-propagation networks in the modelling of the TIG welding process, Journal of Materials Process Technology 75 (1988) 54-62.
  • [8] D.S. Nagesh, G.L. Datta, Prediction of weld bead geometry and prediction in shielded metal-arc welding using artificial neural networks, Journal of Materials Process Technology 79 (2002) 1-10.
  • [9] P. Li, M.T.C. Fang, J. Lucas, Modelling of submerged arc welding bead using self-adaptive offset neural network, Journal of Materials Process Technology 71 (1997) 228-298.
  • [10] Y.S. Tang, H.L. Tsai. S.S. Yeh, Modelling, optimization and classification of weld quality in tungsten inert Gas welding, International Journal of Machine Tools and Manufacturing 39 (1999) 1427-1438.
  • [11] J.Y. Jeng, T.F. Mau, S.M. Leu, Prediction of laser butt joint welding parameters using back-propagation and learning vector quantisation networks, Journal of Materials Process Technology 99 (2000) 207-218.
  • [12] I.S. Kim, J.S. Son, P.K. Yarlagadda, A study on the quality improvement of robotic GMA welding process, International Journal of Robotics and Computer Integrated Manufacturing 19/6 (2003) 567-572.
  • [13] I.S. Kim, J.S. Son, C.E. Park, P.K. Yarlagadda, An investigation into an intelligent system for predicting bead geometry in GMA welding process, Materials Processing Technology 159 (2004) 113-118.
  • [14] R. Battiti, First and second order methods for learning, Between steepest descent and Newton's method, Neural Computation 4/2 (1992) 141-166.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-201111a5-6e15-4a7d-a4d1-93aac688f7cd
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