Transactions of the Indian Institute of Metals

, Volume 71, Issue 12, pp 3063–3075 | Cite as

Performance Evaluation of Arc Welding Process Using Weld Data Analysis

  • Vikas KumarEmail author
  • S. K. Albert
  • N. Chandrasekhar
  • J. Jayapandian
Technical Paper


Shielded metal arc welding (SMAW) and metal inert gas (GMAW) welding process are the two most widely used welding processes. These processes are widely used for the construction and fabrication purpose in almost all type of industries. Some of the important factors which govern the weld quality in these welding processes are welding power sources, role of shielding gas (for GMAW process), welding consumables and skill of the welders. Currently, effects of these factors are evaluated by examining the quality of the weld produced and not by monitoring how welding process is affected by change in these factors. This is an indirect method because actual contribution made by individual parameter in physical process is effectively ignored. Further, this is expensive and time-consuming as the assessment can be carried out only after the weld is completed. Hence, a procedure to assess the quality of welding process using the data acquired while welding is in progress is preferred to testing of the weld for this purpose. In both SMAW and GMAW processes, welding speed, voltage and current are important parameters that affect the quality of the welds. Among these, monitoring of welding speed is relatively easy; but monitoring voltage and current is not. This is because, welding is a stochastic process in which wide variation in voltage and current occurs and duration of these variations is so short that they are not observed in the voltage and current displayed in the power source. However, with the help of a high-speed data acquisition system, voltage and current variations during actual welding process can be recorded and subsequently analysed to reveal very useful information on the welding process, and subsequently quality analysis of individual welding parameters can also be done. In the present study, the voltage and current signals acquired using a digital storage oscilloscope have been used to study SMAW and GMAW processes. Data was acquired for duration of 20 s at a sampling rate of 100,000 samples/s while welding is in progress. In the case of SMAW process, welding data was acquired for welds made using different welding power sources, but with same welder and same type of electrode. In the case of GMAW process, welds were made using same wire and same welder but with different gases for shielding and at different set currents. Dynamic variation in the voltage and current signals were carefully studied using time domain and statistical analyses. Results showed that differences in the characteristics of the different power sources used for SMAW process and effect of shielding gases and arc current on GMAW process could be easily revealed by such analysis. For SMAW process, results obtained could also be correlated with the appearance of the weld beads. Hence, a procedure involving high-speed data acquisition of voltage and current signal while welding is in progress and the statistical analysis of the acquired data have been proposed for monitoring of these two arc welding processes.


Arc welding process Data acquisition and processing Statistical analysis Arc welding electrodes 



We thank Dr. A.K.Bhaduri, Director Indira Gandhi Centre for Atomic Research, kalpakkam and Mr. T.S. Ravichandarn of ICF (Integral Coach Factory, Chennai) for their support and encouragement during this whole study. We also acknowledge the support received from our colleagues in materials technology division and CWD in conducting welding trials.


  1. 1.
    Lanzoni M, Salomoni M, and Riccò B, Weld J 89 (2010) 218.Google Scholar
  2. 2.
    Luksa K, and Rymarski Z, J Achiev Mater Manuf Eng 17 (2006) 377.Google Scholar
  3. 3.
    Lebar A, Selak L, and Butala P, Strojniški Vestnik J Mech Eng 58 (2012) 444.Google Scholar
  4. 4.
    Adolfsson S, Ericson K, and Grennberg A, Mech Syst Signal Process 10 (1996) 633.CrossRefGoogle Scholar
  5. 5.
    Adolfsson S, Bahrami A, and Claesson I, Proceedings of TENCO’96, Digital Signal Processing Applications, Vol. 2, IEEE, New York, NY (1996), p 635.Google Scholar
  6. 6.
    Siewert T, Samardžić I, Klarić Š, Proceedings of 1st. DAAAM International Conference on Advanced Technologies for Developing Countries, Sl. Brod, UDK 621, ISBN 3-901509-32-1 (2002), p 227.Google Scholar
  7. 7.
    Wu C S, Gao J Q, and Hu J K, Institute for Materials Joining, Shandong University, Jinan, People’s Republic of China, January 2007.Google Scholar
  8. 8.
    Kumar V, Chandrasekhar N, Albert S K, Jayapandian J, Measurement (2016) 1.CrossRefGoogle Scholar
  9. 9.
    Kumar V, Chandrasekhar N, Albert S K, Jayapandian J, Measurement (2018) 114.CrossRefGoogle Scholar
  10. 10.
    Kumar V, Albert S K, Chandrasekhar N, Jayapandian J, IEEE First International Conference on Control, Measurement and Instrumentation, Kolkata (CMI) (2016), p 196.Google Scholar
  11. 11.
    Rehfeldt D, and Rehfeldt M D, J Chem Pharm Sci (2015) 274.Google Scholar
  12. 12.
    Chen B, Han F, Hhuang Y, Lu K, Llu Y, Li L, Weld J (2009) 99.Google Scholar
  13. 13.
    Sumesh A, Rameshkumar K, Raja A, Mohandas K, Santhakumari A, Shyambabu R, Arab J Sci Eng (2017) 4649.CrossRefGoogle Scholar
  14. 14.
    Bing X, IEEE Proceedings 3rd Third International Conference on Measuring Technology and Mechatronics Automation (ICMTMA) (2011), p 1150.Google Scholar
  15. 15.
    Rehfeldt D, Rehfeldt M, Proceedings International Forum on Automobile Welding, Mechanical Engineering Press, Beijing (2003).Google Scholar
  16. 16.
    Heald P R, Madigan R B, Siewert T A, and Liu S, Weld J (1994) 38s.Google Scholar
  17. 17.
    Ramirez J E, and Johnson M, Weld J (2010) 232s.Google Scholar
  18. 18.
    Simpson S W, Sci Technol Weld Join 12 (2007) 481.CrossRefGoogle Scholar
  19. 19.
    Simpson S W, Sci Technol Weld Join 13 (2008) 87.CrossRefGoogle Scholar
  20. 20.
    Pal S, Pal S K, Samantray A K, Int J Comput Integr Manuf, 23 (2010) 453.CrossRefGoogle Scholar
  21. 21.
    Wu C S, Hu Q X, Sun S, Polte T, and Rehfeldt D, Proc Instrum Mech Eng B, J Eng Manuf 218 (2004) 1145.CrossRefGoogle Scholar
  22. 22.
    Wu C S, Polte T, and Rehfeldt D, Weld J 80 (2001) 16s.Google Scholar
  23. 23.
    Wu C S, Polte T, and Rehfeldt D, Sci Technol Weld Join 5 (2000) 324.CrossRefGoogle Scholar
  24. 24.
    Pal S, Pal S K, and Samantaray A K, Sci Technol Weld Join 13 (2008) 638.CrossRefGoogle Scholar
  25. 25.
  26. 26.
    Norrish J, Advanced Welding Processes, Institute of Physics Publishing, London (1992).Google Scholar
  27. 27.
    O’Brien R L, Welding Hand Book 8th Edition Volume 2, Welding Process, American Welding Society, Miami, FL (1991).Google Scholar
  28. 28.
    Craig E, Gas Metal Arc and Flux Cored Welding Parameters, A unique Approach to Parameters, Weld Quality, and Weld Costs, Weldtrain (1991).Google Scholar

Copyright information

© The Indian Institute of Metals - IIM 2018

Authors and Affiliations

  1. 1.School of Electronics EngineeringKalinga Institute of Industrial TechnologyBhubaneswarIndia
  2. 2.Homi Bhabha National InstituteMumbaiIndia
  3. 3.Materials Engineering GroupIndira Gandhi Centre for Atomic ResearchKalpakkamIndia

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