Optimization of the Tungsten Inert Gas Process Parameters using Response Surface Methodology
Optimization is a very important techniques applied in the manufacturing industry that utilizes mathematical and artificial intelligence methods. The complexity associated with most optimization techniques have resulted to search for new ones. This search has led to the emergence of response surface methodology (RSM). The paper aims to optimize tungsten inert gas process parameters required to eliminate post-weld crack formation and stabilize heat input in mild steel weldment using RSM. The main input variables considered are voltage, current and speed whereas the response parameter is Brinell hardness number (BHN). The statistical design of experiment was done using the central composite design technique. The experiment was implemented 20 times with 5 specimens per experiment. The responses were measured, recorded and optimized using RSM. From the results, it was observed that a voltage of 21.95 V, current of 190.0 A, and welding speed of 5.00 mm/s produced a weld material with the following optimal properties; BHN (200.959 HAZ), heat input (1.69076 kJ/mm), cooling rate (72.07 /s), preheat temperature (150.68 ) and amount of diffusible hydrogen (12.36 mL/100g). The optimal solution was selected by design expert with a desirability value of 95.40 %.
M. Kimchi, X. Sun, E. V. Stephens, M. A. Khaleel, and H. Shao, “Resistance spot welding of aluminum alloy to steel with transition material from process to performance part I: Experimental study” Welding Journal, vol. 2, pp. 188-195, 2002.
K. J. Tarun, B. Bhuvnesh, B. Kulbhushan, and S. Varun, “Prediction and optimization of weld bead geometry in gas metal arc welding process using RSM” International Journal of Science, Engineering and Technology, vol. 2, no. 7, pp. 34-42, 2019.
P. Sreeraj, T. Kannan, and M. Subhasis, “Optimization of weld bead geometry for stainless steel cladding deposited by GMAW,” American Journal of Engineering Research, vol. 2, no. 5, pp. 178-187, 2018.
R. Storn, and K. Price, “Differential evolution - A simple and efficient heuristic for global optimization over continuous spaces,” Journal of Global Optimization, vol. 11, no. 4, pp. 341–359, 2015.
S. Panagiotidou, and G. Tagaras, “Optimal preventive maintenance for equipment with two quality states and general failure time distributions,” European Journal of Operational Research, vol. 180, no. 1, pp. 329-353, 2016.
S. Pathiyasseril, T. Kannan, and M. Subhasis, “Prediction and control of weld bead geometry in gas metal arc welding process using genetic algorithm,” European Journal of Operational Research, vol. 13, no. 4, pp. 87-94, 2013.
F. J. Cerino-Cordova, A. M. Garcia-Leon, R. B. Garcia-Reyes, M. T. Garza-Gonzalez, E. Soto-Regalado, M. N. Sanchez-Gonzalez, and I. Quezada-Lopez, “Response surface methodology for lead biosorption on Aspergillus Tesseus,” International Journal of Environmental Science and Technology, vol. 8, no 4, pp. 695-704 2014.
J. Edwin Raja Dhas, S. Jenkins Hexley Dhas, “A review on optimization of welding process,” Procedia Engineering, vol. 38, pp. 544-554, 2012.
D. Kim, S. Rhee, and H. Park, “Modeling and optimization of a GMA welding process by genetic algorithm and response surface methodology,” International Journal of Production Research, vol. 40, no. 7, pp. 1699-1711, 2010.
N. A. Imhansoloeva, J. I. Achebo, K. Obahiagbon, J. O. Osarenmwinda, and C. E. Etin-Osa, “Optimization of the deposition rate of tungsten inert gas mild steel using response surface methodology,” Engineering, vol. 10, pp. 784-804, 2018.
M. D. Faseeulla Khan, D. K. Dwivedi, and S. Sharma, “Development of response surface model for tensile shear strength of weld-bonds of aluminum alloy 6061 T651,” Materials and Design, vol. 34, pp. 673-678, 2012.
G. Padmanaban, and V. Balasubramanian, “Optimization of pulsed current gas tungsten arc welding process parameters to attain maximum tensile strength in AZ31B magnesium alloy,” Transaction of Nonferrous Metals Society of China, vol. 21, pp. 467-476, 2016.
N. Bradley, “The response surface methodology,” M.A. thesis, Indiana University of South Bend, 2007, http://hdl.handle.net/2022/16795 (accessed April 2, 2021).
P. Pondi, J. Achebo, and A. Ozigagun, “Prediction of tungsten inert gas welding process parameters using design of experiment and fuzzy logic,” Journal of Advance in Science and Engineering, vol. 4, no. 2, pp. 86-97, 2021.
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