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Joint strength prediction in a pulsed MIG welding process using hybrid neuro ant colony-optimized model

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Abstract

In this work, a pulsed metal inert gas welding (PMIGW) process is modeled by using a hybrid soft computing technique. Ant colony optimization (ACO) and back-propagation neural network (BPNN) models are combined to predict the ultimate tensile strength of butt-welded joints. A large number of experiments have been conducted, and comparative study shows that the hybrid neuro ant colony-optimized model produces faster and also better weld-joint strength prediction than the conventional back propagation model.

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References

  1. Amin M (1983) Pulsed current parameters for stability and controlled metal transfer in arc welding. Met Constr 15(5):272–278

    Google Scholar 

  2. Sweet LM (1985) Sensor-based control systems for arc welding robots. Robot Comput-Integr Manuf 2:125–133

    Article  Google Scholar 

  3. Sforza P, Blasiis DD (2002) On-line monitoring system for arc welding. NDT&E international 35:37–43. doi:10.1016/S0963-8695(01)00021-4

    Article  Google Scholar 

  4. Munezane Y, Watanabe T, Iochi A, Sekino T (1987) A sensing system for arc welding. In: Lane JD (ed) Robotic welding. IFS, London, pp 265–274

    Google Scholar 

  5. Cook GE, Andersen K, Fernandez KR, Shepard ME, Wells AM (1987) Electric arc sensing for robot positioning control. In: Lane JD (ed) Robotic welding. IFS, London, pp 182–216

    Google Scholar 

  6. Hughes RV, Walduck RP (1987) Electromagnetic arc path control I plasma welding. In: Lane JD (ed) Robotic welding. IFS, London, pp 2244–2263

    Google Scholar 

  7. Nagarajan S, Chen WH, Chin BA (1989) Infrared sensing for adaptive arc welding. Weld J 68:462s–466s

    Google Scholar 

  8. Chen WH, Chin BA (1990) Monitoring weld penetration using infrared sensing techniques. Weld J 69:181s–185s

    Google Scholar 

  9. Guu AC, Rokhlin SI (1992) Arc weld process control using radiographic sensing. Mater Eval 50:1344–1348

    Google Scholar 

  10. International institute of welding (1985) Automation and robotisation in welding and allied processes. Pergamon, Oxford

    Google Scholar 

  11. Grad L, Grum J, Polajnar I, Slabe JM (2004) Feasibility study of acoustic signals for on-line monitoring in short circuit gas metal arc welding. Int J Mach Tools Manuf 44:555–561. doi:10.1016/j.ijmachtools.2003.10.016

    Article  Google Scholar 

  12. Saini D, Floyd S (1998) An investigation of gas metal arc welding sound signature for on-line quality control. Weld J 77:172s–179s

    Google Scholar 

  13. Carlson NM, Johnson JA (1988) Ultrasonic sensing of weld pool penetration. Weld J 67:239s–246s

    Google Scholar 

  14. Li D, Yonglun S, Feng Y (2000) On line monitoring of weld defects for short-circuit gas metal arc welding based on the self-organize feature map neural networks. Proc int. joint. conf. neural networks 5:239–244

    Google Scholar 

  15. Siewert T, Samardzic I, Klaric S (2002) Application of an on-line weld monitoring system. Proceedings of the 1st int. conf. on advanced technologies for developing countries. Slavonski Brod, Croatia

  16. Cook GE (1981) Feed back and adaptive control in automated arc welding systems. Met Constr 13:551–556

    Google Scholar 

  17. Quinn TP, Smith C, Mccowan CN, Blachowiak E, Madigan RB (1999) Arc sensing for defects in constant voltage gas metal arc welding. Weld J 79:322s–328s

    Google Scholar 

  18. Johnson JA, Carlson NM, Smartt HB, Clark DE (1991) Process control of GMAW: sensing of metal transfer mode. Weld J 70:91s–99s

    Google Scholar 

  19. Rajasekaran S, Kulkarni SD, Mallya UD, Chaturvedi RC (1998) Droplet detachment and plate fusion characteristics in pulsed current gas metal arc welding. Weld J 78:254s–269s

    Google Scholar 

  20. Adolfsson S, Bahrami A, Bolmsjo G, Claesson I (1999) On-line quality monitoring in short-circuit gas metal arc welding. Weld J 78:59s–73s

    Google Scholar 

  21. Wang J, Kusumoto K, Nezu K (2003) Microweld penetration monitoring techniques by arc sensing. Proc int. conf. on advanced intelligent mechatronics, IEEE/ASME1027–1030

  22. Chu YX, Hu SJ, Hou WK, Wang PC, Marin SP (2004) Signature analysis for quality monitoring in short circuit GMAW. Weld J 83:336s–343s

    Google Scholar 

  23. Andersen K, Cook GE, Karsai G, Ramaswamy K (1990) Artificial neural networks applied to arc welding process modeling and control. IEEE Transactions on Industry Applications 26:824–830. doi:10.1109/28.60056

    Article  Google Scholar 

  24. Cook GE, Barnett RJ, Andersen K, Strauss AM (1995) Welding modeling and controlling using artificial neural networks. Industry applications, IEEE Trans 31:1484–1491

    Article  Google Scholar 

  25. Kang SI, Kim GH, Lee SB (1999) A study on the horizontal fillet welding using neural networks. 3rd Int. conf. on knowledge-based intelligent information. Eng Sys, Australia, pp 217–221

    Google Scholar 

  26. Lee J, Um K (2000) A comparison in a back-bead predication of gas metal arc welding using multiple regression analysis and artificial neural network. Optical and Laser in Eng 34:149–158. doi:10.1016/S0143-8166(00)00097-X

    Article  Google Scholar 

  27. Chi SC, Hsu LC (2001) A fuzzy radial basis function neural network for predicting multiple quality characteristics of plasma arc welding. IFSA/NAFIPS, Vancouver Canada, pp 2807–2812

    Google Scholar 

  28. Di L, Srikanthan T, Chandel RS, Katsunori I (2001) Neural-network based self-organized fuzzy logic control for arc welding. Eng Appl Artif Intell 14:115–124

    Article  Google Scholar 

  29. Nagesh DS, Datta GL (2002) Prediction of weld bead geometry and prenetration in shielded metal-arc welding using artifical neural networks. J Mater Process Technol 123:303–312. doi:10.1016/S0924-0136(02)00101-2

    Article  Google Scholar 

  30. Kim IS, Son JS, Lee SH, Yarlagadda PKDV (2004) Optimal design of neural networks for control in robotics arc welding. Robot Comput-Integr Manuf 20:57–63

    Article  MATH  Google Scholar 

  31. Lightfoot MP, Bruce GJ, McPherson NA, Woods K (2005) The application of artificial neural networks to weld-induced deformation in ship plate. Weld J 84:23s–30s

    Google Scholar 

  32. Pal S, Pal SK, Samantaray AK (2007) Radial basis function neural network model based prediction of weld-plate distortion due to pulsed metal inert gas welding. Sc Tech Welding and Joining 12:725–731. doi:10.1179/174329307X249351

    Article  Google Scholar 

  33. Kim IS, Jeong YJ, Lee CW, Yarlagadda PKDV (2003) Prediction of welding parameters for pipeline welding using an intelligent system. Int J Adv Manuf Technol 22:713–719

    Article  Google Scholar 

  34. Quero JM, Millan RL, Franquelo LG (1994) Neural network approach to weld quality monitoring. Proc. 20th int. conf. on industrial electronics, control and instrumentation. IEEE 2:1287–1291

    Google Scholar 

  35. Ohshima K, Yabe M, Akita K, Kugai K, Kubota T, Yamane S (1995) Sensor fusion using neural network in robotic welding. Industry Applications Conf., IEEE 2:1764–1769

    Google Scholar 

  36. Jang GB, Kim HK, Kang SS (2001) The effects of root opening on mechanical properties, deformation and residual stress of weldments. Weld J 81:80–89

    Google Scholar 

  37. Canyurt OE (2005) Estimation of welded joint strength using genetic algorithm approach. Int J Mech Sc 47:1249–1261. doi:10.1016/j.ijmecsci.2005.04.001

    Article  Google Scholar 

  38. Cool T, Bhadeshia HKDH, Mackay DJC (1997) The yield and ultimate tensile strength of steel welds. J Mater Sc Engg A 223:186–200. doi:10.1016/S0921-5093(96)10513-X

    Article  Google Scholar 

  39. Pal S, Pal SK, Samantaray AK (2008) Artificial neural network modeling of weld joint strength prediction of a pulsed metal inert gas welding process using arc signals. J Mater Pro Tech 202:464–474. doi:10.1016/j.jmatprotec.2007.09.039

  40. Takeshita K (2000) Model equation for predicting the tensile strength of resistance-brazed joints. Weld J 80:261s–265s

    Google Scholar 

  41. Engelbrecht AP (2005) Fundamentals of computational swarm intelligence. Wiley, New York

    Google Scholar 

  42. Su-bing Z, Ze-min L (2001) Neural network training using ant algorithm in ATM traffic control. Proceedings of the IEEE international symposium on circuits and systems, Australia 3:157–160

    Google Scholar 

  43. Blum C, Socha K (2005) Training feed-forward neural networks with ant colony optimization: an application to pattern classification. Fifth IEEE international conference on hybrid intelligent systems 233–238

  44. Liu YP, Wu MG, Qian JX (2007) Predicting coal ash fusion temperature based on its chemical composition using ACO-BP neural network. Thermochimica Acta 454:64–68. doi:10.1016/j.tca.2006.10.026

    Article  Google Scholar 

  45. Haykin S (2003) Neural Networks: a comprehensive foundation. 2nd edn, Prentice Hall, Upper Saddle River

  46. Dorigo M, Maniezzo V, Colorni A (1996) The ant system: optimization by a colony of co-operating agents. IEEE Transaction on Systems, Man and Cybernatics- Part B 26(1):29–41

    Article  Google Scholar 

  47. Johnson S (2001) Emergence: the connected lives of ants, brains, cities, and software. Scribner, New York

    Google Scholar 

  48. Applegate DL, Bixby RE, Chvátal V, Cook WJ (2006) The traveling salesman problem: a computational study. Princeton University Press, Princeton

    MATH  Google Scholar 

  49. Montgomery DC (1976) Design and analysis of experiments. Wiley, New York

    Google Scholar 

  50. Minitab Inc., User manual of MINITAB™ statistical software, Release 13.31, State College, PA 16801 USA, 2000

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Authors and Affiliations

  1. Department of Mechanical Engineering, Indian Institute of Technology, Kharagpur, 721 302, India

    N. Raghavendra, Rakshit Koranne, Sukhomay Pal, Surjya K. Pal & Arun K. Samantaray

Authors
  1. N. Raghavendra
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  2. Rakshit Koranne
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  3. Sukhomay Pal
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  4. Surjya K. Pal
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  5. Arun K. Samantaray
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Correspondence to Surjya K. Pal.

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Raghavendra, N., Koranne, R., Pal, S. et al. Joint strength prediction in a pulsed MIG welding process using hybrid neuro ant colony-optimized model. Int J Adv Manuf Technol 41, 694–705 (2009). https://doi.org/10.1007/s00170-008-1517-2

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  • DOI: https://doi.org/10.1007/s00170-008-1517-2

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