Unconstrained Optimization for Maximizing Ultimate Tensile Strength of Pulsed Current Micro Plasma Arc Welded Inconel 625 Sheets

  • Kondapalli Siva Prasad
  • Y. V. Srinivasa Murthy
  • Ch. Srinivasa Rao
  • D. Nageswara Rao
  • Gurrala Jagadish
Conference paper
Part of the Advances in Intelligent and Soft Computing book series (AINSC, volume 132)


Nickel alloys had gathered wide acceptance in the fabrication of components which require high temperature resistance and corrosion resistance. The paper focuses on developing mathematical model to predict ultimate tensile strength of pulsed current micro plasma arc welded Inconel 625 nickel alloy. Four factors, five level, central composite rotatable design matrix is used to optimize the number of experiments. The mathematical model has been developed by response surface method and its adequacy is checked by ANOVA technique. By using the developed mathematical model, ultimate tensile strength of the weld joints can be predicted with 99% confidence level. Contour plots are drawn to study the interaction effect of welding parameters on ultimate tensile strength of Inconel 625 weld joints. The developed mathematical model has been optimized using Hooke and Jeeves Method to maximize the ultimate tensile strength.


Ultimate Tensile Strength Weld Joint Response Surface Method Exploratory Move Develop Mathematical Model 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Balasubramanian, B., Jayabalan, V., Balasubramanian, V.: Optimizing the Pulsed Current Gas Tungsten Arc Welding Parameters. J. Mater Sci. Technol. 22, 821–821 (2002)Google Scholar
  2. 2.
    Madusudhana Reddy, G., Gokhale, A.A., Prasad Rao, K.: Weld microstructure refinement in a 1441 grade aluminium-lithium alloy. Journal of Material Science 32, 4117–4122 (1997)CrossRefGoogle Scholar
  3. 3.
    Zhang, D.K., Niu, J.T.: Application of Artificial Neural Network modeling to Plasma Arc Welding of Aluminum alloys. Journal of Advanced Metallurgical Sciences 13, 194–200 (2000)MathSciNetGoogle Scholar
  4. 4.
    Chi, S.-C., Hsu, L.-C.: A fuzzy Radial Basis Function Neural Network for Predicting Multiple Quality characteristics of Plasma Arc Welding. IEEE, pp. 2807–2812 (2001) 0-7803-7078-3/01Google Scholar
  5. 5.
    Hsiao, Y.F., Tarng, Y.S., Wang, J.: Huang Optimization of Plasma Arc Welding Parameters by Using the Taguchi Method with the Grey Relational Analysis. Journal of Materials and Manufacturing Processes 23, 11–18 (2008)Google Scholar
  6. 6.
    Siva, K., Muragan, N., Logesh, R.: Optimization of weld bead geometry in Plasma transferred arc hardfacing austenitic stainless steel plates using genetic algorithm. Int. J. Adv. Manuf. Technol. 41, 24–30 (2008)CrossRefGoogle Scholar
  7. 7.
    Lakshinarayana, A.K., Balasubramanian, V., Varahamoorthy, R., Babu, S.: Predicted the Dilution of Plasma Transferred Arc Hardfacing of Stellite on Carbon Steel using Response Surface Methodology. Metals and Materials International 14, 779–789 (2008)CrossRefGoogle Scholar
  8. 8.
    Balasubramanian, V., Lakshminarayanan, A.K., Varahamoorthy, R., Babu, S.: Application of Response Surface Methodology to Prediction of Dilution in Plasma Transferred Arc Hardfacing of Stainless Steel on Carbon Steel. Science Direct 12, 44–53 (2009)Google Scholar
  9. 9.
    Montgomery, D.C.: Design and analysis of experiments, 3rd edn., pp. 291–291. John Wiley & Sons, New York (1991)zbMATHGoogle Scholar
  10. 10.
    BoxG, E.P., Hunter, W.H., Hunter, J.S.: Statistics for experiments, pp. 112–111. John Wiley & Sons, New York (1978)Google Scholar
  11. 11.
    Ravindra, J., Parmar, R.S.: Mathematical model to predict weld bead geometry for flux cored arc welding. Journal of Metal Construction 19, 12–41 (1987)Google Scholar
  12. 12.
    Cochran, W.G., Cox, G.M.: Experimental Designs. John Wiley & Sons Inc., London (1957)zbMATHGoogle Scholar
  13. 13.
    Barker, T.B.: Quality by experimental design. ASQC Quality Press, Marcel Dekker (1981)Google Scholar
  14. 14.
    Gardiner, W.P., Gettinby, G.: Experimental design techniques in statistical practice. Horwood press, Chichester (1998)Google Scholar
  15. 15.
    Kalyanmoy, D.: Optimization for engineering design. Prentice Hall (1988)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Kondapalli Siva Prasad
    • 1
  • Y. V. Srinivasa Murthy
    • 2
  • Ch. Srinivasa Rao
    • 3
  • D. Nageswara Rao
    • 4
  • Gurrala Jagadish
    • 2
  1. 1.Department of Mechanical EngineeringAnil Neerukonda Institute of Technology & SciencesVisakhapatnamIndia
  2. 2.Department of Computer Science & EngineeringAnil Neerukonda Institute of Technology & SciencesVisakhapatnamIndia
  3. 3.Department of Mechanical EngineeringAndhra UniversityVisakhapatnamIndia
  4. 4.Centurion UniversityIndia

Personalised recommendations