# Development of mathematical models for prediction of weld bead geometry in cladding by flux cored arc welding

• P. K. Palani
• N. Murugan
Original Article

## Abstract

The mechanical and corrosion resistant properties of cladded components depend on the clad bead geometries, which in turn are controlled by the process parameters. Therefore it is essential to study the effect of process parameters on the bead geometry to enable effective control of these parameters. The above objective can easily be achieved by developing equations to predict the weld bead dimensions in terms of process parameters. Experiments were conducted to develop models, using a three factor, five level factorial design for 317L flux cored stainless steel wire with IS:2062 structural steel as base plate. The models so developed were checked for their adequacy. Confirmation experiments were also conducted and the results show that the models developed can predict the bead geometries and dilution with reasonable accuracy. It was observed from the investigation that the interactive effect of the process parameters on the bead geometry is significant and cannot be neglected.

## Notes

### Acknowledgements

The authors wish to thank the All India Council for Technical Education, New Delhi and University Grant Commission, New Delhi, India for their financial support for procuring the equipment and materials. The authors also wish to thank M/S Böhler Thyssen Welding, Austria, for sponsoring the 317L flux cored wire to carry out this investigation.

## References

1. 1.
Alam N, Jarvis L, Harris D, Solta A (2002) Laser cladding for repair of engineering components. Aust Weld J 47:38–47Google Scholar
2. 2.
Missori S, Murdolo F, Sili A (2004) Single-pass laser beam welding of clad steel plate. Weld J 83(2):65s–71sGoogle Scholar
3. 3.
Murugan N, Parmer RS (1995) Mathematical models for bead geometry prediction in austenitic stainless steel surfacing by MIG welding. Int J Join Mater 7(23):71–80Google Scholar
4. 4.
Kim I-S, Son J-S, Jeung Y-J (2001) Control and optimisation of bead width for multi-pass welding in robotic arc welding processes. Aust Weld J 46:43–46Google Scholar
5. 5.
Kang MJ, Kim YS, Ahn, Rhee S (2003) Spatter rate estimation in the short circuit transfer region of GMAW. Weld J 82(9):238s–247sGoogle Scholar
6. 6.
Juang SC, Tarng (2002) Process parameter selection for optimizing the weld pool geometry in the tungsten inert gas welding of stainless steel. J Mater Process Technol 122:33–37
7. 7.
Kim IS, Son KJ, Yang YS, Yaragada PKDV (2003) Sensitivity analysis for process parameters in GMA welding process using factorial design method. Int Jf Mach Tools Manuf 43:763–769
8. 8.
Subramaniam S, White DR, Jones JE, Lyons DW (1999) Experimental approach to selection of pulsing parameters in pulsed GMAW, AWS. Weld J 78(5):166-s–172-sGoogle Scholar
9. 9.
Allen TT, Richardson RW, Tagliabue DP, Maul GP (2002) Statistaical process design for robotic GMA welding of sheet metal. Weld J 81(5):69s–76sGoogle Scholar
10. 10.
Murugan N, Parmer RS (1997) Stainless steel cladding deposited by automatic gas metal arc welding. Weld J 76(10):391-s-402-sGoogle Scholar
11. 11.
Montgomery DC, Runger GC (1999) Applied statistics and probability for engineers, 2nd edn. Wiley, New YorkGoogle Scholar
12. 12.
Walpole RE, Myers RH (1998) Probability and statistics for engineers and scientists, 6th edn. Prentice Hall, Upper Saddle River, NJGoogle Scholar
13. 13.
Cheremisinoff NP (1987) Practical statistics for engineers and scientists. Technomic Publishing, Lancaster, PAGoogle Scholar
14. 14.
Cochran WG, Cox GM (1957) Experimental designs, 2nd edn, Wiley, Singapore
15. 15.
Khuri AI, Cornell JA (1996) Response surfaces, designs and analyses. Marcell Dekker, New York
16. 16.
Montgomery DC (2001) Design and analysis of experiments, 5th edn. Wiley, New YorkGoogle Scholar
17. 17.
Rmasamy S, Gould J, Workman D (2002) Design-of-experiments study to examine the effect of polarity on stud welding. Weld J 81(2):19s–26sGoogle Scholar