Journal of Welding and Joining

The Korean Welding and Joining Society (대한용접접합학회)
권호 표지
DOI QR코드

DOI QR Code

Expectation of Bead Shape using Non-linear Multiple Regression and Piecewise Cubic Hermite Interpolation in FCA Fillet Pipe Welding

FCA 필릿 파이프 용접에서 다중 비선형 회귀 모형과 구간적 3차 에르미트 보간법을 통한 비드 형상 예측

  • 조대원 (한국과학기술원 기계공학과) ;
  • 나석주 (한국과학기술원 기계공학과) ;
  • 이목영 (포항산업과학연구원 융합공정연구그룹)
  • Published : 2009.10.31
Download PDF
⟨ Previous Next ⟩

Abstract

Pipe welding is used in various ranges such as civil engineering and ship building engineering. Until now, many technicians work for pipe welding manually under harmful, dangerous and difficult conditions. So it is necessary to install automation process. For automation pipe welding, relation between welding parameters & bead shape should be considered. Using this relation, bead shape could be expected from welding parameters. FCAW was used in this study. Instead of pipe workpiece, fillet joint plate is used, which were inclined 0,45,90,135,180 degree. By analyzing between welding parameters (current, welding speed, voltage) and bead shape parameters with non-linear multiple regression, bead shape parameters could be expected. Piecewise Cubic Hermite Interpolation was used to expect smooth curved bead shape with bead shape parameters. From these processes, bead shape could be expected from welding parameters.

Keywords

References

  1. K. M. Tay and C. Butler "Modeling and optimizing of a MIG welding process-a case study using experimental designs and neural networks." Quality and Reliability Engineering International 132 (1997), 61–70 https://doi.org/10.1002/(SICI)1099-1638
  2. D. S. Nageshet al "Prediction of weld bead geometry and penetration in shielded metal-arc welding using artificial neural networks" Journal of Materials Processing Technology 123 (2002), 303–312 https://doi.org/10.1016/S0924-0136(02)00101-2
  3. I.S Kim, J.S. Son, S.H. Lee and Prasad K.D.V. Yarlagadda, "Optimal design of neural networks for control in robotic arc welding" Robotics and Computer-Integrated Manufacturing 20 (2004), 57–63 https://doi.org/10.1016/S0736-5845(03)00068-1
  4. Hakan Ates, "Prediction of gas metal arc welding parameters based on artificial neural networks' Journal of Materials Processing Technology 123 (2002), 303–312 https://doi.org/10.1016/S0924-0136(02)00101-2
  5. H. S. Moon, S. Y. Lee and S. J. Na, "A Study on Selection of Gas Metal Arc Welding Parameters of Fillet Joints Using Neural Network" Journal of the Korean Welding Society, 11-4, (1993), 151-163 (in Korean)
  6. H. S. Moon, "A study on selection of Adequate Welding Parameters by Considering Weld Bead Shape and Weld Defect and Development of Seam Tracker in Horizontal Fillet Welding". Ph.D. Thesis, Kaist, 1997, 49-82
  7. Y. S. Tarng and W. H. Yang, "Optimization of the Weld Bead Geometry in Gas Tungsten Arc Welding by the Taguchi Method" International Journal of Advanced Manufacturing Technology, 14 (1998), 549-554 https://doi.org/10.1007/BF01301698
  8. G.E. Ridings, R.C. Thomson and G. Thewlis, "Prediction of multiwire submerged arc weld bead shape using neural network modelling" Science and Technology of Welding & Joining, 7-5 (2002), 265-279 https://doi.org/10.1179/136217102225006822
  9. K.M.Tay and C. Butler, "Modelling and optimizing of mig welding process - A case study using experimental designs and neural networks" Quality and Reliability Engineering International 13-2 (1997), 61-70 https://doi.org/10.1002/(SICI)1099-1638(199703)13:2<61::AID-QRE69>3.0.CO;2-Y
  10. I.S. Kim, J.S. Son, C.E. Park, I.J. Kim and H.H. Kim, "An investigation into an intelligent system for predicting bead geometry in GMA welding process" Journal of Materials Processing Technology 159 (2005), 113–118 https://doi.org/10.1016/j.jmatprotec.2004.04.415
  11. E.Kreyszig 'Advanced engineering mathematics, 8th edition', 862-864
  12. WG Bickley, "Piecewise cubic interpolation and two point boundary problems" The Computer Journal, 1968
  13. P.K,Palan and N.Murugan, "Optimization of weld bead geometry for stainless steel claddings deposited by FCAW" Journal of Materials Processing Technology 190 (2007), 291-299 https://doi.org/10.1016/j.jmatprotec.2007.02.035