A STOCHASTIC SEARCH ALGORITHM TO OPTIMIZE THE PENETRATION AREA OF MILD STEEL WELD

Authors

  • B. OFOEYENO Department of Production Engineering, University of Benin, Edo State, Nigeria.
  • J. I. ACHEBO Department of Production Engineering, University of Benin, Edo State, Nigeria.
  • A. OZIGAGUN Department of Production Engineering, University of Benin, Edo State, Nigeria.

Keywords:

tungsten inert gas welding, area of penetration, welding speed, mild steel, stochastic search algorithm

Abstract

Welding is still the most popular means of fabrication available to the marine, aviation, and automobile manufacturing industries. It becomes important to understand the inter relationship between welding process parameters and weld bead geometries such as penetration depth, penetration size ratio and area of penetration. To achieve this goal, Mild Steel plates weld specimen were fabricated by using tungsten inert gas welding process which is one of the most widely used welding methods. The area of penetration was measured for each specimen after the welding process and the stochastic search algorithm was used to optimize the results It was found that at high welding speeds the area of penetration increased. The optimal solution selected solution 18 as the best optimal solution obtained from the genetic algorithm model.

References

Chandel, R.S., Seow, H.P., Cheong, F.L. (1997): Effect of increasing deposition rate on the bead geometry of submerged arc welds. – Journal of Materials Processing Technology 72(1): 124-128.

Chandel, R.S. (1988): Mathematical modelling of gas metal arc weld features. – Proceedings of the Fourth International Conference on Modeling of Casting and Welding Processes 11p.

Dhindsa, G.S., Sandhu, G.S., Singh, J., Saini, P.K. (2016): Effect of activated TIG welding process on depth of penetration in carbon steel (SA516 GR-70). – International Journal of Engineering Science Invention Research & Development 2(11): 727-733.

Funderburk, R.S. (1999): Key concepts in welding engineering. – Welding Innovation 16(1): 1-4.

Heiple, C.R., Roper, J.R. (1982): Mechanism for minor element effect on GTA fusion zone geometry. – WELD 61(4): 97-102.

Lee, J.I., Um, K.W. (2000): A prediction of welding process parameters by prediction of back-bead geometry. – Journal of materials processing technology 108(1): 106-113.

Lowke, J.J., Tanaka, M., Ushio, M. (2005): Mechanisms giving increased weld depth due to a flux. – Journal of physics D: applied physics 38(18): 3438-3445.

Sudhakaran, R., Murugan, V.V., Sivasakthivel, P.S. (2012): Optimization of process parameters to minimize angular distortion in gas tungsten arc welded stainless steel 202 grade plates using particle swarm optimization. – Journal of Engineering Science and Technology 7(2): 195-208.

Tewari, S.P., Gupta, A., Prakash, J. (2010): Effect of welding parameters on the weldability of material. – International Journal of Engineering Science and Technology 2(4): 512-516.

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Published

2020-12-08

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Section

Articles

How to Cite

A STOCHASTIC SEARCH ALGORITHM TO OPTIMIZE THE PENETRATION AREA OF MILD STEEL WELD. (2020). Quantum Journal of Engineering, Science and Technology, 1(3), 31-38. https://www.qjoest.com/index.php/qjoest/article/view/12