Application of Multi-Criteria Decision Making Optimization Tool for Determining Mild Steel Weld Properties and Process Parameters Using the TOPSIS
International Journal of Materials Science and Applications
Volume 4, Issue 3, May 2015, Pages: 149-158
Received: Feb. 8, 2015;
Accepted: Mar. 24, 2015;
Published: Apr. 24, 2015
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Joseph Achebo, Department of Production Engineering, University of Benin, Benin City, Edo State, Nigeria
Monday Omoregie, Department of Production Engineering, University of Benin, Benin City, Edo State, Nigeria
Several different processes and models have been adopted for the optimization of weld deposit quality of mild steel joints. These various processes and models have been used continually over the decades to find new ways of improving weld deposit quality, with the ultimate aim of improving the service life of the resulting weld joints. This quest to find ways of improving weld deposit quality has resulted in the use of Technique for Order Preference by Similarity to Ideal Solution (TOPSIS). TOPSIS is one such technique used for solving multi criteria problems. It is based on the concept that the optimal alternative should have the shortest distance from the positive ideal solution, and the farthest distance from the negative ideal solution. From applying the TOPSIS technique, it was found that weldment 9 has the best weld mechanical properties with a Brinell hardness number (BHN) of 216, Ultimate tensile strength (UTS) of 600MPa, Charpy V-notch (CVN) impact energy of 90J, and a percentage elongation of 23%. Also the relationship between the input parameters and the output parameters was examined. It is therefore, concluded that TOPSIS has successfully optimized the input process parameters which has produced the most desired mechanical properties. In this study a step by step approach for the application of the TOPSIS technique is adopted.
Application of Multi-Criteria Decision Making Optimization Tool for Determining Mild Steel Weld Properties and Process Parameters Using the TOPSIS, International Journal of Materials Science and Applications.
Vol. 4, No. 3,
2015, pp. 149-158.
F. Kolahan and M. Heidari. A new approach for predicting and optimizing weld bead geometry in GMAW, Intl J Mech Syst Sci and Eng, 2, 2010, pp138 – 142.
J.I. Achebo. Optimization of fluence energy in relation to weld properties based on vogel approximation method. World Congr on Eng: Intl Conf on Mech Eng, London , July 4 – 6, 2012
K.Y. Benyounis and A.G Olabi. Optimization of different welding processes using statistical and numerical approaches- A reference guide. Adv in eng software, Elsevier, 39(6): 2008, pp483-496. DOI: 10.1016/j.advengsoft.2007.03.012.
N. Murugan and R.S. Parmar, Effect of welding conditions on microstructure and properties of tupe 316L stainless steel submerged arc welding cladding, Weld J, AWS, 76 (5): 1997, pp210-s- 220-s.
H.Yamaguchi, K. Ogawa and K. Sakaguchi. Optimization of friction welding condition of 5056 aluminium alloy, J. Japan Inst of Light metal, 41(10): 1991, pp716-720.
K.Y. Benyounis, A.H. Bettamer, A.G. Olabi and M.S.J. Hashmi. Prediction the impact strength of spiral welded pipe joints in submerged arc welding of low carbon steel, Proc of IMC21, Limerick 1-3-Sept 2004. pp 200-210.
M. Sen, M. Mukherjee. and T.K. Pal.. Prediction of weld bead geometry for double pulse gas metal arc welding process by regression analysis. 5th International & 26th All India Manufacturing Technology, Design and Research Conference, December 12th–14th, 2014 IIT Guwahati, Assam, India, pp. 814-6.
H. Okuyucu, A. Kurt, and E. Arcaklioglu. Artificial neural network application to the friction stir welding of aluminium plates, J. Mater & Design, 29: 2007, pp78-84.
Y. Wei, H.K.D. Bhadeshia and T. Sourmail. Mechanical property prediction of commercially pure titanium welds with artificial neural network, J. Mater Sci Techn, 21(3): 2005, pp403-407.
K.S. Prasad, C.S. Rao, and D.N. Rao. Optimizing pulsed current micro plasma arc welding parameters to maximize ultimate tensile strength of inconel 625 nickel alloy using response surface method. Intl J Eng Sci and Techn, 3(6): 2011, 26 - 236
R. Wang. Performance evaluation method-technique for order preference by similarity to ideal solution (TOPSIS). http//researcher.most.gov.tw/public/caroljoe/Data/02182133671.ppt. Accessed 22 September 2014.
D. Ozturk and F. Batuk. Technique for order preference by similarity to ideal solution (TOPSIS) for spatial decision problems. Proc ISPRS. 2011. http://www.isprs.org/proceedings/2011/Gi4DM/PDF/PP12.pdf
J. Malczewski. GIS and Multicriteria Decision Analysis. Wiley, New York. 1999.
J. Ananda and G. Herath. Analysis of forest policy using multi-attribute value theory. In: Using multi-criteria decision analysis in natural resource management, G. Herath and T. Prato (eds.), Ashgate Publishing Ltd., Hampshire, 2006, pp. 11-40.
P. Kaur, R. Sharma, N.C. Mahanti and A.K. Singh. Exploration of topsis (Technique for order preference by similarity to ideal solution) as an alternative to traditional classification algorithm in small areas of lohardaga district of Jharkhand, India using remote sensing image-a case study. Res J Earth Sci 1(2): 2009, pp81-85.
D. Wu and D.L. Olson. A TOPSIS data mining demonstration and application to credit scoring. Intl J Data Warehous & Min, 2(3), 1-10, July-September, 2006.
G.R. Jahanshahloo, L.F. Hosseinzadeh and M. Izadikhah. Extension of the TOPSIS method for decision-making problems with fuzzy data. Applied Maths and Comput 181: 2006, pp1544–1551.