Skip to main content
SpringerLink
Log in

Welding Process Optimization Methods: A Review

  • Conference paper
  • First Online:
Transactions on Intelligent Welding Manufacturing

Part of the book series: Transactions on Intelligent Welding Manufacturing ((TRINWM))

Abstract

Tuning welding parameters in welding processes is very challenging because there are different welding process parameters and welding performance indices. Typical manual welding parameter tuning methods are time-consuming and tedious; moreover, weld quality depends on the welders’ skills. To deal with the parameter tuning problem, many welding process optimization methods have been proposed. Even though there are some review papers to summarize these methods, there is a lack of a systematic way to analyze and summarize these optimization methods. In this paper, the welding process optimization methods are categorized into open-loop methods and closed-loop methods. Some methods in these two categories are reviewed and summarized.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 129.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 169.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 169.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Deshpande M (2017) On optimization of GMAW process parameters to improve the length of penetration in en 10025 S 235 grade. J Weld Join 35:74–78

    Article  Google Scholar 

  2. Dixit R, Kaushik K, Mittal P (2018) Modeling, analysis & optimization of parameters for great weld strength of the chassis for off-road vehicles. Int Res J Eng Technol 5(5):1907–1915

    Google Scholar 

  3. Joseph J, Muthukumaran S (2017) Optimization of activated TIG welding parameters for improving weld joint strength of aisi 4135 PM steel by genetic algorithm and simulated annealing. Int J Adv Manuf Technol 93(1):23–34

    Article  Google Scholar 

  4. Jurica M, Kozuh Z, Garasic I et al (2018) Optimization of the a TIG welding for stainless steels. IOP Conf Ser: Mater Sci Eng 329(1):1–9

    Google Scholar 

  5. Kumar A, Khurana MK, Yadav PK (2016) Optimization of gas metal arc welding process parameters. IOP Conf Ser: Mater Sci Eng 149(1):1–11

    Google Scholar 

  6. Kumar V, Goyal N (2018) Parametric optimization of metal inert gas welding for hot die steel by using Taguchi approach. Mater Sci Res India 15(1):100–106

    Article  Google Scholar 

  7. Pandhare V (2016) Determination of significant factors affecting the bending strength of weld joint prepared by gas metal arc welding. Int J Mech Eng Res Technol 2(1):1–10

    Google Scholar 

  8. Srirangan AK, Paulraj S (2016) Multi-response optimization of process parameters for TIG welding of incoloy 800HT by Taguchi grey relational analysis. Eng Sci Technol Int J 19(2):811–817

    Article  Google Scholar 

  9. Acherjee B, Misra D, Bose D et al (2009) Prediction of weld strength and seam width for laser transmission welding of thermoplastic using response surface methodology. Laser Technol 41(8):956–967

    Article  Google Scholar 

  10. Beal VE, Erasenthiran P, Hopkinson N et al (2006) Optimization of processing parameters in laser fused h13/cu materials using response surface method (RSM). J Mater Process Technol 174(1–3):145–154

    Article  Google Scholar 

  11. Karthikeyan R, Balasubramanian V, Fan C et al (2010) Predictions of the optimized friction stir spot welding process parameters for joining AA2024 aluminum alloy using RSM. Manuf Technol 51(1–4):173–183

    Article  Google Scholar 

  12. Khan MF, Dwivedi D, Sharma S (2012) Development of response surface model for tensile shear strength of weld-bonds of aluminium alloy 6061 T651. Mater Des 34:673–678

    Article  Google Scholar 

  13. Kim T, Park H, Rhee S (2005) Optimization of welding parameters for resistance spot welding of trip steel with response surface methodology. Int J Prod Res 43(21):30–36

    Article  Google Scholar 

  14. Kohli A, Singh H (2011) Optimization of processing parameters in induction hardening using response surface methodology. Indian Acad Sci 36(2):141–152

    Google Scholar 

  15. Kuar A, Paul G, Mitra S (2006) Nd: YAG laser micromachining of alumina–aluminium interpenetrating phase composite using response surface methodology. Mach Mater 1(4):432–444

    Google Scholar 

  16. Muhammad N, Manurung YHP, Hafidzi M et al (2012) Optimization and modeling of spot welding parameters with simultaneous multiple response consideration using multi-objective Taguchi method and RSM. J Mech Sci Technol 26(8):2365–2370

    Article  Google Scholar 

  17. Srivastava S, Garg R (2017) Process parameter optimization of gas metal arc welding on IS:2062 mild steel using response surface methodology. J Manuf Process 25:296–305

    Article  Google Scholar 

  18. Zhao Y, Zhang Y, Hu W et al (2012) Optimization of laser welding thin-gage galvanized steel via response surface methodology. Lasers Eng 50(9):1267–1273

    Article  Google Scholar 

  19. Correia DS, Goncalves CV, Cunha SS et al (2005) Comparison between genetic algorithms and response surface methodology in GMAW welding optimization. J Mater Process Technol 160(1):70–76

    Article  Google Scholar 

  20. Correia DS, Gonçalves CV, Sebastião SC Jr et al (2004) GMAW welding optimization using genetic algorithms. J Braz Soc Mech Sci Eng 26(1):28–33

    Article  Google Scholar 

  21. Kim D, Rhee S (2001) Optimization of arc welding process parameters using a genetic algorithm. Weld J 80(7):184–198

    Google Scholar 

  22. Pavel E, Roland J, Vladimír V (2018) Optimization of welding process using a genetic algorithm. J Mech Eng 68(2):17–24

    Google Scholar 

  23. Pozo LP, Olivares ZF, Duran AO (2015) Optimization of welding parameters using a genetic algorithm: a robotic arm–assisted implementation for recovery of pelton turbine blades. Adv Mech Eng 7(11):1–17

    Google Scholar 

  24. Vedrtnam A, Singh G, Kumar A (2018) Optimizing submerged arc welding using response surface methodology, regression analysis, and genetic algorithm. Def Technol 14(3):204–212

    Article  Google Scholar 

  25. Arunchai T, Sonthipermpoon K, Tamee K (2014) Resistance spot welding optimization based on artificial neural network. Int J Manuf Eng

    Google Scholar 

  26. Nagesh D, Datta G (2010) 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 10:897–907

    Article  Google Scholar 

  27. Sathiya P, Panneerselvam K, Jaleel MA (2012) Optimization of laser welding process parameters for super austenitic stainless steel using artificial neural networks and genetic algorithm. Mater Des 1980–2015(36):490–498

    Article  Google Scholar 

  28. Dong H et al (2016) Predicting characteristic performance for arc welding process. In: 2016 IEEE international conference on cyber technology in automation, control, and intelligent systems (CYBER). IEEE, Chengdu, pp 7–12

    Google Scholar 

  29. Dong H et al (2017) Real time welding parameter prediction for desired character performance. In: 2017 IEEE international conference on robotics and automation (ICRA). IEEE, Singapore, pp 1794–1799

    Google Scholar 

  30. Rajkumar B, Murugan N (2014) Development of regression models and optimization of FCAW process parameter of 2205 duplex stainless steel. Indian J Eng Mater Sci 21(2):149–154

    Google Scholar 

  31. Sterling D et al (2015) Welding parameter optimization based on Gaussian process regression Bayesian optimization algorithm. In: 2015 IEEE international conference on automation science and engineering (CASE), pp 1490–1496

    Google Scholar 

  32. Padmanaban R, Balusamy V, Saikrishna V (2014) Simulated annealing based parameter optimization for friction stir welding of dissimilar aluminum alloys. Procedia Eng 97:864–870

    Article  Google Scholar 

  33. Sathiya P, Aravindan S, Haq A (2006) Optimization of friction welding parameters using simulated annealing. Indian J Eng Mater Sci 13(1):37–44

    Google Scholar 

  34. Sudhakaran R, Vel Murugan V, Sivasakthivel PS et al, Prediction and optimization of depth of penetration for stainless steel gas tungsten arc welded plates using artificial neural networks and simulated annealing algorithm. Neural Comput Appl 22(3):637–649

    Article  Google Scholar 

  35. Dhas ER, Satheesh M (2012) Multiple object of submerged arc welding process parameters using grey based fuzzy logic. Adv Prod Eng Manag 7:5–16

    Google Scholar 

  36. Katherasan D, Elias JV, Sathiya P et al (2012) Flux cored arc welding parameter optimization using particle swarm optimization algorithm. Procedia Eng 38:3913–3926

    Article  Google Scholar 

  37. Sreeraj P, Kannan T, Maji S (2013) Optimization of GMAW process parameters using particle swarm optimization. ISRN Metall

    Google Scholar 

  38. Patnaik A, Biswas S, Mahapatra S (2007) An evolutionary approach to parameter optimization of submerged arc welding in the hardfacing process. Int J Manuf Res 2(4):462–483

    Article  Google Scholar 

  39. Sathiya P, Aravindan S, Haq AN et al (2009) Optimization of friction welding parameters using evolutionary computational techniques. J Mater Process Technol 209(5):2576–2584

    Article  Google Scholar 

  40. Tutum CC et al (2016) Surrogate-based evolutionary optimization for friction stir welding. In: Proceedings of IEEE world congress on computational intelligence (WCCI 2016), Vancouver, BC, Canada

    Google Scholar 

  41. Dhas JER, Dhas SJH (2012) A review on optimization of welding process. Procedia Eng 38:544–554

    Article  Google Scholar 

  42. Angaria S, Rao PS, Dham SS (2018) Optimization of MIG welding process parameters: a review. Res J Eng Technol 4(3):273–276

    Article  Google Scholar 

  43. Batta A, Aggarwal JK, Khurana V et al (2015) Optimization of submerged arc welding process: a review. IOSR J Mech Civ Eng 12(2):39–44

    Google Scholar 

  44. Zhang Y, Kovacevic R, Li L (1996) Characterization and real-time measurement of geometrical appearance of the weld pool. Int J Mach Tools Manuf 36(7):799–816

    Article  Google Scholar 

  45. Balaram N, Balakrishna B (2013) Characteristics optimization of different welding processes on duplex stainless steels using statistical approach and Taguchi technique—a review guide. Int J Eng Invent 2(3):26–34

    Google Scholar 

  46. Chan CW, Man HM, Yue TM (2015) Parameter optimization for laser welding of NITI wires by the Taguchi method. Lasers Eng 30:247–265

    Google Scholar 

  47. Chauhan V, Jadoun RS (2014) Parametric optimization of MIG welding for stainless steel (ss-304) and low carbon steel using Taguchi design method. Int J Adv Technol Eng Res (IJATER) 2(1):224–229

    Google Scholar 

  48. Chavda SP, Desai JV, Patel TM (2014) A review on optimization of MIG welding parameters using Taguchi DOE method. Int J Eng Manag Res 4(1):2250–2258

    Google Scholar 

  49. Elfar OMR et al (2016) Process parameters optimization for friction stir welding of pure aluminium to brass (cuzn30) using Taguchi technique. In: MATEC web of conferences, vol 43. EDP Sciences

    Google Scholar 

  50. Koilraj M, Sundareswaran V, Vijayan S et al (2012) Friction stir welding of dissimilar aluminum alloys AA2219 to AA5083—optimization of process parameters using Taguchi technique. Mater Des 42:1–7

    Article  Google Scholar 

  51. Kumanan S, Dhas J, Gowthaman K (2007) Determination of submerged arc welding process parameters using Taguchi method and regression analysis. Indian J Eng Mater Sci 14(3):177–183

    Google Scholar 

  52. Lin H, Chou T, Chou C (2007) Optimation of resistance spot welding process using Taguchi method and a neural network. Exp Tech 31:30–36

    Article  Google Scholar 

  53. Saha A et al (2015) Optimization of process parameters in submerged arc welding using multi-objectives Taguchi method. In: Narayanan R, Dixit U (eds) Advances in material forming and joining. Topics in mining, metallurgy and materials engineering. Springer, New Delhi, pp 221–232

    Chapter  Google Scholar 

  54. Schneider C, Lisboa C, Silva R et al (2017) Optimizing the parameters of TIG-MIG mag hybrid welding on the geometry of bead welding using the Taguchi method. J Manuf Mater Process 1(2):14

    Google Scholar 

  55. Tarng Y, Yang W (1998) Optimization of the weld bead geometry in gas tungsten arc welding by the Taguchi method. Int J Adv Manuf Technol 14(8):549–554

    Article  Google Scholar 

  56. Hargopal G, Ravindrareddy P, Mohanreddy GC et al (2011) Parameter design for MIG welding of Al 65032 alloy using Taguchi technique. J Sci Ind Res 70:844–850

    Google Scholar 

  57. Narwadkar A, Bhosle S (2016) Optimization of MIG welding parameters to control the angular distortion in Fe410WA steel. Mater Manuf Process 31(16):2158–2164

    Article  Google Scholar 

  58. Magudeeswaran G, Nair SR, Sundar L et al (2014) Optimization of process parameters of the activated tungsten inert gas welding for aspect ratio of UNS S32205 duplex stainless steel welds. Def Technol 10(3):251–260

    Article  Google Scholar 

  59. Kumar S, Singh P, Patel D et al (2017) Optimization of TIG welding process parameters using Taguchi’s analysis and response surface methodology. Int J Mech Eng Technol 8(11):932–941

    Google Scholar 

  60. Manjarekar S, Gangavane S, Kadam R et al (2017) Optimization of submerged arc welding (saw) parameters. Int Eng Res J (IERJ) (special issue):73–76

    Google Scholar 

  61. Vinodh S, Karthik Bharathi S, Gopi N (2016) Parametric optimization of submerged arc welding using Taguchi method. Springer, pp 183–194

    Google Scholar 

  62. Bozkurt Y (2012) The optimization of friction STIR welding process parameters to achieve maximum tensile strength in polyethylene sheets. Mater Des 35:440–445

    Article  Google Scholar 

  63. Shanmugarajan B, Rishabh S, Sathiya P (2016) Optimization of laser welding parameters for welding of p92 material using Taguchi based grey relational analysis. Def Technol 12(4):343–350

    Article  Google Scholar 

  64. Ni X, Zhou Z, Wen X et al (2011) The use of Taguchi method to optimize the laser welding of sealing neuro-stimulator. Opt Lasers Eng 49(3):297–304

    Google Scholar 

  65. Kumaran S et al (2018) Optimization of parameters involved in robotic MIG welding process based on quality responses. IOP Conf Ser: Mater Sci Eng 402. IOP Publishing Ltd

    Google Scholar 

  66. Rahimi M, Shayganmanesh M, Noorossana R et al (2018) Modelling and optimization of laser engraving qualitative characteristics of Al-SiC composite using response surface methodology and artificial neural networks. Opt Laser Technol 112:65–76

    Article  Google Scholar 

  67. Narayana A, Srihari T (2012) Optimization of weld bead geometry in MIG welding process using response surface methodology. Int J Sci Technol 2(4):27–34

    Google Scholar 

  68. Angaria S, Rao PS, Dhami SS (2017) Application of RSM to optimize MIG welding process parameters for hardness. Int J Emerg Technol Eng Res 5(9):31–36

    Google Scholar 

  69. Liao B, Shi Y, Cui Y et al (2018) Mathematical model for prediction and optimization of weld bead geometry in all-position automatic welding of pipes. Metals 8(10):756–772

    Article  Google Scholar 

  70. Vasantharaja P, Vasudevan M (2018) Optimization of a-TIG welding process parameters for rafm steel using response surface methodology. Proc Inst Mech Eng Part L: J Mater: Des Appl 232(2):121–136

    Google Scholar 

  71. Kiaee N, Aghaie-Khafri M (2014) Optimization of gas tungsten arc welding process by response surface methodology. Mater Des 54:25–31

    Article  Google Scholar 

  72. Jelokhani-Niaraki MR, Mostafa Arab NB, Naffakh-Moosavy H et al (2016) The systematic parameter optimization in the nd:yag laser beam welding of inconel 625. Int J Adv Manuf Technol 84(9):2537–2546

    Article  Google Scholar 

  73. Ganesamoorthi B, Kalaivanan S, Dinesh R et al (2015) Optimization technique using response surface method for USMW process. Procedia—Soc Behav Sci 189:169–174

    Article  Google Scholar 

  74. Missori S, Sili A, Ucciardello N (2008) Process parameters optimization of laser beam welded joints by neural network. Mater Manuf Process 23(2):168–173

    Article  Google Scholar 

  75. Anand K, Barik BK, Tamilmannan K et al (2015) Artificial neural network modeling studies to predict the friction welding process parameters of Incoloy 800H joints. Eng Sci Technol Int J 18(3):394–407

    Article  Google Scholar 

  76. Singh R, Gupta R, Sarkar S (2012) Prediction of weld width of shielded metal arc weld under magnetic field using artificial neural networks. Int J Comput Eng Res 3(1):58–64

    Google Scholar 

  77. Kumar A, Jadoun R, Bist A (2014) Optimization of mig welding parameters using artificial neural network (ANN) and genetic algorithm (GA). Int J Eng Sci Res Technol 3:1–852

    Google Scholar 

  78. Sarkar A, Majumder A, Pawa M et al (2014) Optimization of process parameters of submerged arc welding by using grey-fuzzy-based Taguchi method for AISI 1518 grade steel. Proc Inst Mech Eng Part B: J Eng Manuf 228(11):1491–1500

    Article  Google Scholar 

  79. Moradpour M, Hashemi S, Khalili K (2015) Multi-objective optimization of welding parameters in submerged arc welding of API X65 steel plates. J Iron Steel Res Int 22(9):870–878

    Article  Google Scholar 

  80. Giri A, Pandey C, Mahapatra M (2018) Achieving optimized tungsten inert gas butt welding conditions of thin cold rolled steel sheets by response surface methodology and artificial neural networks. Proc Inst Mech Eng Part E: J Process Mech Eng 232(4):459–470

    Article  Google Scholar 

  81. Kolahan F, Heidari M (2010) Deling and optimization of mag welding for gas pipelines using regression analysis and simulated annealing algorithm. ANALYSIS J Sci Ind Res 69(04):177–183

    Google Scholar 

  82. Kumar A, Maheshwari S, Sharma SK (2015) Fuzzy logic optimization of weld properties for saw using silica based agglomerated flux. Procedia Comput Sci 57:1140–1148

    Article  Google Scholar 

  83. Mondal P, Bose D (2015) Optimization of the process parameters for mig welding of aisi 304 and is 1079 using fuzzy logic method. Int Res J Eng Technol 2:483–488

    Google Scholar 

  84. Sreeraj P, Kannan T, Maji S (2013) Simulated annealing algorithm for optimization of welding variables for percentage of dilution and application of ANN for prediction of weld bead geometry in GMAW process. Int J Eng Res Appl 3:3913–3926

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Texas State University, San Marcos, TX, 78666, USA

    Heping Chen

  2. US Corporate Research, ABB, Inc, Bloomfield, CT, 06002, USA

    Biao Zhang & Thomas Fuhlbrigge

Authors
  1. Heping Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Biao Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Thomas Fuhlbrigge
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Heping Chen .

Editor information

Editors and Affiliations

  1. Shanghai Jiao Tong University, Shanghai, China

    Shanben Chen

  2. Department of Electrical and Computer Engineering, University of Kentucky, Lexington, KY, USA

    Yuming Zhang

  3. Oak Ridge National Laboratory, Oak Ridge, TN, USA

    Zhili Feng

Rights and permissions

Reprints and permissions

Copyright information

© 2020 Springer Nature Singapore Pte Ltd.

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Chen, H., Zhang, B., Fuhlbrigge, T. (2020). Welding Process Optimization Methods: A Review. In: Chen, S., Zhang, Y., Feng, Z. (eds) Transactions on Intelligent Welding Manufacturing. Transactions on Intelligent Welding Manufacturing. Springer, Singapore. https://doi.org/10.1007/978-981-13-8192-8_1

Download citation

Publish with us

Policies and ethics

Search

Navigation