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 ENG  Vol.11 No.2 , February 2019
Prediction of Weld Penetration Size Factor (WPSF) of TIG Mild Steel Weldment Using Fuzzy Logic
Abstract: Predicting weld responses is a very important but difficult area in the field of welding which can greatly reduce the overall cost of try and error method for any fabrication industry. Fuzzy logic expert tool was used to predict the weld penetration size factor of a weld. The aim of this study is to predict the weld penetration size factor (WPSF) of TIG mild steel welds using fuzzy logic. In this study, the weld specimens were produced using the TIG welding process guided by the central composite experimental design, and thereafter the weld penetration size factor (WPSF) was measured. The process parameters include the voltage, current, gas flow rate and welding speed. The model’s significance, strength and adequacy were checked; for fuzzy logic, fuzzification was done using fuzzy linguistic variable, fuzzy linguistic terms and membership function after which an inference was made based on a set of rules and the output result was defuzzified to a crisp output. Fuzzy logic predicted beyond the boundaries of the given range of parameters. The model developed has proven to be very effective in predicting responses even before actual weld is initiated.
Cite this paper: Stephanie, N. and Ifeanyi, A. (2019) Prediction of Weld Penetration Size Factor (WPSF) of TIG Mild Steel Weldment Using Fuzzy Logic. Engineering, 11, 119-130. doi: 10.4236/eng.2019.112010.
References

[1]   Mistry, P.J. (2016) Effect of Process Parameters on Bead Geometry and Shape Relationship of Gas Metal Arc Weldments. International Journal of Advanced Research in Mechanical Engineering & Technology, 2, 24-27.

[2]   Kumar, V. (2011) Modeling of Weld Bead Geometry and Shape Relationships in submerged Arc Welding Using Developed Fluxes. Jordan Journal of Mechanical and Industrial Engineering, 5, 461-470.

[3]   Narayama, A. and Srihari, T. (2011) Optimization of Weld Bead Geometry in MIG Welding Process Using Response Surface Methodology. International Journal of Science and Technology, 2, 27-34.

[4]   Dhasand, J.E.R. and Satheesh, M. (2013) Sensitivity Analysis of Submerged Arc Welding Parameters for Low Alloy Steel Weldment. Indian Journal of Engineering and Materials Sciences, 20, 425-434.

[5]   Omajene, J.E., Martikainen, J. and Kah, P. (2014) Effect of Welding Parameters on Weld Bead Shape for Welds Done Underwater. International Journal of Mechanical Engineeering and Applications, 2, 128-134.
https://doi.org/10.11648/j.ijmea.20140206.17

[6]   Choudhary, D., Jindal, S. and Mehta, N.P. (2011) To Study the Effect of Welding Parameters on Weld Bead Geometry in SAW Welding Process. Elixir Mech. Engg, 40, 5519-5524.

[7]   Gadewar, S.P., Swaminadhan, P., Harkare, M.G. and Gawande, S.H. (2010) Experimental Investigation of Weld Characteristics for a Single Pass TIG Welding with SS304. International Journal of Engineering Science and Technology, 2, 3676-3686.

[8]   Dutta, P. and Partilhar, D.K. (2007) Modeling of TIG Welding Process Using Conventional Regression Analysis and Neural Network-Based Approaches. Journal of Material Processing Technology, 184, 56-68.
https://doi.org/10.1016/j.jmatprotec.2006.11.004

[9]   Benyonnis, K.Y. and Olabi, A.G. (2008) Optimization of Different Welding Processes Using Statistical and Numerical Approaches. Advances in Engineering Software, 39, 483-496.
https://doi.org/10.1016/j.advengsoft.2007.03.012

[10]   Esme, U., Bayramoglu, M., Kazancoglu, Y. and Ozgun, S. (2009) Optimization of Weld Bead Geometry in TIG Welding Process Using Grey Relation Analysis and Taguchi Method. Materials and Technology, 43, 143-149.

[11]   Kim, I.S., Son, J.S., Kim, H.H., Kim, I.J. and Kang, B.Y. (2006) A Study on Fuzzy Logic Theory to Predict the Process Parameters in GMA Welding Process. Materials Science Forum, 505-507, 541-546.
https://doi.org/10.4028/www.scientific.net/MSF.505-507.541

 
 
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