American Journal of Modeling and Optimization
ISSN (Print): 2333-1143 ISSN (Online): 2333-1267 Website: http://www.sciepub.com/journal/ajmo Editor-in-chief: Dr Anil Kumar Gupta
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American Journal of Modeling and Optimization. 2018, 6(1), 18-34
DOI: 10.12691/ajmo-6-1-2
Open AccessArticle

Development of Artificial Neural Network Models for Predicting Weld Output Parameters in Advanced Fusion Welding of a Magnesium Alloy

K.M. Oluwasegun1, , O.A Ojo1, O.T Ola2, A. Birur3, J. Cuddy3 and K. Chan4

1Department of Mechanical Engineering, University of Manitoba, Winnipeg, Manitoba, Canada

2Technology Access Centre for Aerospace and Manufacturing, Red River College of Applied Arts, Science and Technology, Winnipeg, Manitoba, Canada

3Standard Aero Limited, Winnipeg, Manitoba, Canada

4Huys Limited, Toronto, Ontario, Canada

Pub. Date: October 19, 2018

Cite this paper:
K.M. Oluwasegun, O.A Ojo, O.T Ola, A. Birur, J. Cuddy and K. Chan. Development of Artificial Neural Network Models for Predicting Weld Output Parameters in Advanced Fusion Welding of a Magnesium Alloy. American Journal of Modeling and Optimization. 2018; 6(1):18-34. doi: 10.12691/ajmo-6-1-2

Abstract

This paper describes the development of artificial neural network (ANN) models and multi-response optimization technique to predict and select the best welding parameters during Hybrid Laser Arc Welding (HLAW), Hot Wire Cladding (HWC) and Cold Metal Transfer (CMT) of ZE41-T5 alloy. To predict the performance characteristics, namely; weld depth, underfill, percentage defect and total accumulated pore length, artificial neural network models were developed using Levenberg-Marquardt algorithm. ZE41-T5 was selected as the material to be welded with AZ61 alloy as filler material. Experiments were planned using a 3-factor central composite design and were performed under different welding conditions of laser power, travel speed, wire feed rate, current and frequency. The responses were optimized concurrently using ANN Levenberg-Marquardt algorithm. Finally, experimental confirmations were carried out to identify the effectiveness of ANN. A good agreement was obtained between the experimental output data and ANN predicted results.

Keywords:
hybrid laser arc welding hot wire cladding cold metal transfer artificial neural network optimisation

Creative CommonsThis work is licensed under a Creative Commons Attribution 4.0 International License. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/

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References:

[1]  Pollock TM. (2010). Science 328 (5981): 986-987.
 
[2]  Wise M, Calvin K, Thomson A, Clarke L, Bond-Lamberty B, Sands R, Smith SJ, Janetos A, Edmonds J. (2009). Science 324 (5931): 1183-1186.
 
[3]  Kump LR. (2002). Nature 419: 188-190.
 
[4]  Agnew WG. (1974). Science 183 (4122): 254-256.
 
[5]  Begum S, Chen DL, Xu S, Luo AA. (2009). International Journal of Fatigue 31 (4): 726-735.
 
[6]  Begum S, Chen DL, Xu S, Luo AA. (2009). Metallurgical and Materials Transaction A 40(1): 255-267.
 
[7]  Behler K, Berkmanns J, Ehrhardt A, Frohn W. (1997). Materials & Design 18(4-6): 261-267.
 
[8]  Schubert E, Klassen M, Zerner I J, Walz C, Sepold G. (2001). Journal of Materials Processing Technology 115 (1): 2-8.
 
[9]  Cao X, Jahazi M, Immarigeon JP, Wallace W J. (2006). Journal of Materials Processing Technology 171 (2): 188-204.
 
[10]  Weisheit A, Galun R, Mordike BL. (1998). Welding Journal 77 (4): 149-154.
 
[11]  Zhao H, DebRoy T. (2001). Welding Journal 80 (8): 204-210.
 
[12]  Sun Z, Pan D, Wei J. (2002). Science and Technology of Welding and Joining 7: 343-351.
 
[13]  Zhang J, Shan J G, Ren J.L, Wen P. (2013). Welding Journal 92 (8): 101-109.
 
[14]  Roepke C, Liu S, Kelly S, Martukanitz R. (2010). Welding Journal 89 (7): 140-149.
 
[15]  Sachez-Amaya JM, Boukha Z, Amaya-Vazquez MR, Botana FJ. (2012). Welding Journal 91 (5):155-161.
 
[16]  Victor B, Farson DF, Ream S Walters CT. (2011). Welding Journal 90 (6):113-120.
 
[17]  Matsunawa A, Kim JD, Seto N, Mizutani M, Katayama S J. (1998). Journal of Laser Applications 10 (6,): 247-254.
 
[18]  Tucker JD, Nolan TK, Martin AJ, Young GA. (2012). JOM 64 (12):1409-1417.
 
[19]  Madison J D, Aagesen L K. (2012). Scripta Materialia 67 (9). 783-786.
 
[20]  Salminen A, Phiili H, Purtonen, T. (2010). Journal of Mechanical Engineering Science 224(5): 1019-1029.
 
[21]  Harvilla D, Rominger V, Holzer M, Harrer T, Andrew A, Advanced Welding Techniques with Optimized Accessories for High Brightness 1µm Lasers,” in High-Power Laser Materials Processing: Lasers, Beam Delivery, Diagnostics, and Applications II, Proceedings of SPIE, (2013) 8603.
 
[22]  Ilar T, Eriksson I, Powell J, Kaplan A. (2012). Physics Procedia 39:27-32.
 
[23]  Blecher JJ, Palmer TA, Debroy T. (2015). Welding Journal 94 (3): 73-82.
 
[24]  Hu B, Richardson IM. (2006). Welding in the World 50 (7-8): 51-57.
 
[25]  Maamar H, Otmani RR, Fahssi T, Debbache N, Allou D. (2008). Hradec and Moravici-METAL, 5: 13-15.
 
[26]  Kim JS, Watanabe T, Yoshida Y. (1995). Journal of Material Science Letters 14 (22): 1624-1626.
 
[27]  Ola OT, Doern FE. (2014). Materials and Design 57: 51-59.
 
[28]  Matsunawa A, Kim JD Katayama S, Porosity formation in laser welding-mechanisms and suppression methods, International Congress on Applications of Lasers and Electro-Optics-ICALEO. (1997). Miami 73-82.
 
[29]  Shtrikman MM, Pinskiia AV, Filatovb AA, Koshkinb VV, Mezentsevab EA, Guk NV. (2011). Welding International 25 (6): 457-462.
 
[30]  Devletian JH, Wood WE. (1983). Welding Research Council Bulletin 290: 1-18.
 
[31]  Kou S, (2002). Welding metallurgy,” Second edition, New York, John Wiley & Sons, Inc.
 
[32]  Norris JT, Robino CV, Hirschfeld DA, Perricone MJ. (2011). Welding Journal 90:198-203.
 
[33]  Daugherty WJ, Cannell GR. (2003). Practical Failure Analysis 3 (4): 56-62.
 
[34]  Liu L M, Song G, Zhu M S. (2008). Metallurgical and Materials Transaction A, 39A: 1702-1711.
 
[35]  Qui X, Song G. (2010). Materials & Design 31 (1):605-609.
 
[36]  Shahi AS, Pandey S. (2006). Science and Technology of Welding and Joining 11 (6):634-640.
 
[37]  Lv S X, Tian X B, Wang H T, Yang S Q. (2007). Science and Technology of Welding and Joining 12 (5): 431-435.
 
[38]  Prasad V V S, Rao A S, Prakash U, Baligidad R G. (2002). Science and Technology of Welding and Joining 7 (2): 102-106.
 
[39]  Francis J. A. (2002). Science and Technology of Welding and Joining 7 (5): 331-338.
 
[40]  Tuseka J, Suban M J. (2003). Journal of Materials Processing Technology 133 (1-2): 207-213.
 
[41]  Cao X, Jahazi M, Xiao, Immarigeon JP. (2005). Materials and Manufacturing Processes 20, (6): 987-1004.
 
[42]  Pickin C, Young K. (2006). Science and Technology of Welding and Joining 11 (4): 1-3.
 
[43]  Agudo L, Jank N, Wagner J. (2008). Steel research International 79 (7): 530-535.
 
[44]  Pickin C, Williams S, Lunt M. (2011). Journal of Materials Processing Technology 211 (3):496-502.
 
[45]  Widen J, Bergmann JP, Frank H.J. (2006). Journal of Thermal Spray Technology 15 (4):779-784.
 
[46]  Ramanathan K, Periasamy VM, Natarajan U. (2008). Portgaliae Electrochimica Acta 26, (4): 361-368.
 
[47]  Subramaanian S, Periasamy VM, Pushpavanam M, Ramasamy K. (2009). Portgaliae Electrochimica Acta, 27 (1): 47-55.
 
[48]  Haykin S (1999). Neural Networks: A Comprehensive Foundation, 2nd Edition, Prentice Hall, New Jersey.
 
[49]  Reed RD. (1999). Neural Smithing, MIT Press, Cambridge, MA.
 
[50]  Hagan MT, Demuth HB, Beale MH. (1996). Neural Network Design, PWS Publishing Company, Boston, MA.