35
Views
0
CrossRef citations to date
0
Altmetric
Research Articles

Application of response surface methodology and genetic algorithm in the prediction and optimization of the ductile properties of mild steel weld

, , &
Pages 140-152 | Received 13 Nov 2023, Accepted 29 Nov 2023, Published online: 27 Dec 2023

References

  • Allison A, Scudamore R. Strategic research agenda: joining: joining sub-platform. SRA; 2014. www.joining-platform.com.
  • Chastel Y, Passemard L. Joining technologies for future automobile multi-material modules. 11th international conference on technology of plasticity, ICTP. Procedia Eng. 2014;81:2104–2110. doi: 10.1016/j.proeng.2014.10.293
  • Guo ZG, Ma TJ, Yang XW, et al. Vairis, in-situ investigation on dislocation slip concentrated fracture mechanism of linear friction welded dissimilar Ti17(α + β)/Ti17(β) titanium alloy joint. Mater Sci Eng A. 2023;872:144991. doi: 10.1016/j.msea.2023.144991
  • Guo Z, Ma T, Yang X, et al. Multi-scale analyses of phase transformation mechanisms and hardness in linear friction welded Ti17(α + β)/Ti17(β) dissimilar titanium alloy joint. Chin J Aeronaut. 2023;(Online). doi: 10.1016/j.cja.2023.08.018
  • Wahab MA. Manual metal arc welding and gas metal arc welding. In: Hashmi S, editor. Comprehensive materials processing. 1st Edition, vol. 6. Oxford, UK: Elsevier Science and Technology: 2014. p. 60–63.
  • Groover MP. Fundamentals of modern manufacturing: materials, processes, and systems. Third Ed. New York: Wiley, 2007.
  • O’Brien RL. Welding processes, vol. 2 of welding handbook. 8th Ed. Miami: American Welding Society; 1991.
  • Benyounis KY, Olabi AG, Hashmi MSJ. Effect of laser welding parameters on the heat input and weld bead profile. J Mater Process Tech. 2005164–165:978–985. doi: 10.1016/j.jmatprotec.2005.02.060
  • Benyounis KY, Olabi AG. Optimization of different welding processes using statistical and numerical approaches – a reference guide. Adv Eng Softw. 2008;39(6):483–496. doi: 10.1016/j.advengsoft.2007.03.012
  • Giridharan PK, Murugan N. Optimization of pulsed GTA welding process parameters for the welding of AISI 304 L stainless steel sheets. Int J Adv Manuf Technol. 2009;40(5–6):478–489. doi: 10.1007/s00170-008-1373-0
  • Bang KS, Jung DH, Park C, et al. Effect of welding parameters on tensile strength on weld metal flux cored welding. Sci Tech Weld Join. 2008;13(6):508–514.
  • Sada SO. Modeling performance of response surface methodology and artificial neural network. J Appl Sci Environ Manage. 2018;22(6):875–881. doi: 10.4314/jasem.v22i6.6
  • Murugan N, Parmar RS. Effects of MIG process parameters on the geometry of the bead in the automatic surfacing of stainless steel. J of Material Processing Techn. 1994;41(4):381–398. doi: 10.1016/0924-0136(94)90003-5
  • Yu S, Xiawei Y, Dong W, et al. Controlling deformation and residual stresses in a TIG joint for invar steel molds. J Mater Res Technol. 2023;27:490–507.
  • Yu S, Xiawei Y, Dong W, et al. Optimizing welding sequence of TIG cross-joint of invar steel using residual stresses and deformations. J Manuf Processes. 2023;105:232–245. doi: 10.1016/j.jmapro.2023.09.047
  • Talabi SI, Owolabi OB, Adebisi JA, et al. Effect of welding variable variables on mechanical properties of low carbon steel welded joint. Adv Produc Engineer Manag. 2014;9(4):181–186. doi: 10.14743/apem2014.4.186
  • Noordin MY, Venkatesh VC, Sharif S, et al. Application of response surface methodology in describing the performance of coated carbide tools when turning A1511045 steel. J Mater Process Tech. 2004;145(1):46–58. doi: 10.1016/S0924-0136(03)00861-6
  • Modenesi PJ, Avelar RC. The influence of small variations of wire characteristics on gas metal arc welding process stability. J Mater Process Tech. 1999;86(1–3):226–232. doi: 10.1016/S0924-0136(98)00315-X
  • Sada SO. The use of multi-objective genetic algorithm (MOGA) in optimizing and predicting weld quality. Cogent Eng. 2020;7(1):1741310. doi: 10.1080/23311916.2020.1741310
  • Deb K, Anand A, Joshi D. A computationally efficient evolutionary algorithm for real-parameter optimization. Evol Comput. 2002;10(4):371–395. doi: 10.1162/106365602760972767
  • Andersen K, Cook GE, Karsai G, et al. Artificial neural networks applied to arc welding process modeling and control. IEEE Trans on Ind Applicat. 1990;26(5):824–830. doi: 10.1109/28.60056
  • Chokkalingham S, Chandrasekhar N, Vasudevan M. Predicting the depth of penetration and weld bead width from the infra-red thermal image of the weld pool using artificial neural network modeling. J Intell Manuf. 2012;23(5):1995–2001. doi: 10.1007/s10845-011-0526-4
  • Kanti KM, Rao PS. Prediction of bead geometry in pulsed GMA welding using back propagation neural network. J Mater Process Technol. 2008;200(1):300–305.
  • Nagesh D, Datta G. Genetic algorithm for optimization of welding variables for height to width ratio and application of ANN for prediction of bead geometry for TIG welding process. Appl Soft Comput. 2010;10(3):897–907. doi: 10.1016/j.asoc.2009.10.007
  • Akkas N, Karayel D, Ozkan SS, et al. Modeling and analysis of the weld bead geometry in submerged arc welding by using adaptive neuro fuzzy inference system (ANFIS). Math. Problems Eng. 2013;2013:1–10. doi: 10.1155/2013/473495
  • Ghosh A, Mukherjee S, Chattopadhyaya S, et al. Weld bead parametric estimation of SAW process through neural network. IWJ. 2007;40(4):33. doi: 10.22486/iwj.v40i4.178436
  • Li P, Fang MTC, Lucas J. Modelling of submerged arc weld beads using self-adaptive offset neutral networks. J Mater Process Tech. 1997;71(2):288–298. doi: 10.1016/S0924-0136(97)00087-3
  • Mohandas T, Madhusudhan RG, Naveed MA. Comparative evaluation of gas tungsten and shielded metal arc welds of a stainless steel. J Mater Process Tech. 1999;94(2–3):133–140. doi: 10.1016/S0924-0136(99)00092-8
  • Kim IS, Son JS, Park CE, et al. An investigation into an intelligent system for predicting bead geometry in GMA welding process. J Mat Proc Tech. 2005;159(1):113–118. doi: 10.1016/j.jmatprotec.2004.04.415
  • Bharath P, Sridhar VG, Kumar MS. Optimization of 316 stainless steel weld joint characteristics using taguchi technique. 12th global congress on manufacturing and management, GCMM. Procedia Eng. 2014;97:881–891. doi: 10.1016/j.proeng.2014.12.363
  • Ghosh N, Pal PK, Nandi G. Parametric optimization of MIG welding on 316L austenitic stainless steel by grey-based taguchi method. Global colloquium in recent adv. and effectual researches in eng, science and tech. Procedia Tech. 2016;25:1038–1048. doi: 10.1016/j.protcy.2016.08.204
  • Sada SO. Optimization of weld strength properties of tungsten inert gas mild steel welds using the response surface methodology. Nig J Tech. 2018;37(2):407–415. doi: 10.4314/njt.v37i2.15
  • Juang SC, Tarng YS. Process parameters selection for optimizing the weld pool geometry in the tungsten inert gas welding of stainless steel. J Mat Process Tech. 2002;122(1):33–37. doi: 10.1016/S0924-0136(02)00021-3
  • Montgomery DC. Design and analysis of experiments. Eighth ed. New York: Wiley; 2005.
  • Sada SO. Parametric optimization of weld reinforcements using response surface methodology optimization process. J Appl Sci Environ Manage. 2018;22(8):1331–1335. doi: 10.4314/jasem.v22i8.31
  • Rao RV. Jaya: a simple and new optimization algorithm for solving constrained and unconstrained optimization problems. Int J Ind Eng Comput. 2016;7(1):19–34.
  • Sada SO, Achebo J. Optimization and prediction of the weld bead geometry of a mild steel metal inert gas weld. Adv Mater Process Technolog. 2020;8(2):1625–1634. doi: 10.1080/2374068X.2020.1860597
  • Kumar S, Chandna P, Bhushan G. Prediction and optimization of work piece temperature during 2.5-D milling of inconel 625 using regression and genetic algorithm. Cogent Eng. 2020;7(1):1731199. doi: 10.1080/23311916.2020.1731199
  • Kumar R, Hynes NRJ. Prediction and optimization of surface roughness in thermal drilling using integrated ANFIS and GA approach. Eng Sci Technol Int J. 2020;23(1):30–41. doi: 10.1016/j.jestch.2019.04.011
  • Sada SO, Achebo J, Obahiagbon K. Evaluation of the optimal strength and ductility of a mild steel arc welded plate based on the weld design. Weld Int. 2021;35(7-9):261–268. doi: 10.1080/09507116.2021.198784
  • Sada SO, Achebo J. Optimization of the ductile properties of an arc welded plate based on the yield strength, elongation, and modulus of elasticity. J Optim Indust Eng. 2021;14(1):159–167.

Reprints and Corporate Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

To request a reprint or corporate permissions for this article, please click on the relevant link below:

Academic Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

Obtain permissions instantly via Rightslink by clicking on the button below:

If you are unable to obtain permissions via Rightslink, please complete and submit this Permissions form. For more information, please visit our Permissions help page.