Skip to main content

Advertisement

Log in

The use of TOPSIS-based-desirability function approach to optimize the balances among mechanical performances, energy consumption, and production efficiency of the arc welding process

  • ORIGINAL ARTICLE
  • Published:
The International Journal of Advanced Manufacturing Technology Aims and scope Submit manuscript

Abstract

A couple of models were established to investigate the effects of welding parameters (voltage, wire feed speed, and welding speed) on the resultant mechanical properties of welding bead and welding heat input on the basis of a Box-Behnken experimental design (BBD). The three key mechanical parameters, that is, maximum displacement, peak load, and energy absorption, were processed via the technique in order of preference by similarity to ideal solution (TOPSIS) and Shannon entropy technique. After that, the correlations among the mechanical properties, welding heat input, and three technological variables in the welding process were established herein. Analysis of variance (ANOVA) implied that the heat input relied on voltage, welding speed, wire feed rate, and the interaction effects among these factors. These models act as the basis to achieve the multi-objective optimization problem by the desirability function approach. Results suggest that the welding settings favoring a robust trade-off between minimum welding heat input and maximum mechanical properties involve an intermediate value of wire feed speed, a high value of voltage, and welding speed. This welding parameter combination not only can produce an optimum welding bead with robust mechanical performances but also guarantees the goal of optimum welding heat utilization and production efficiency, in which the high level of welding speed is strongly recommended.

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

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11

Similar content being viewed by others

References

  1. Kim IS, Park MH (2018) A review on optimizations of welding parameters in GMA welding process. J Weld Join 36:65–75

    Article  Google Scholar 

  2. Daniyan IA, Mpofu K, Adeodu AO (2019) Optimization of welding parameters using Taguchi and response surface methodology for rail car bracket assembly. Int J Adv Manuf Technol 100:2221–2228

    Article  Google Scholar 

  3. Ghosh N, Pal PK, Nandi G (2017) GMAW dissimilar welding of AISI 409 ferritic stainless steel to AISI 316L austenitic stainless steel by using AISI 308 filler wire. Eng Sci Technol Int J 20:1334–1341

    Google Scholar 

  4. Rizvi SA, Ali W (2018) Optimization of welding parameters and microstructure and fracture mode characterization of GMA welding by using Taguchi method on SS304H austenitic steel. Mech Mechan Eng 22:1121–1131

    Article  Google Scholar 

  5. Abdollahi A, Shamanian M, Golozar MA (2018) Parametric optimization of pulsed current gas arc welding of dissimilar welding between UNS32750 and AISI 321 based on Taguchi method. Trans Indian Inst Metals 71:597–603

    Article  Google Scholar 

  6. Ghosh N, Pal PK, Nandi G (2017) Parametric optimization of gas metal arc welding process by PCA-based Taguchi method on ferritic stainless steel AISI409. Mater Today: Proceed 4:9961–9966

    Google Scholar 

  7. Kumar S, Singh R (2019) Optimization of process parameters of metal inert gas welding with preheating on AISI 1018 mild steel using grey based Taguchi method. Measurement 148:106924

    Article  Google Scholar 

  8. Srivastava S, Garg RK (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 

  9. Martinez-Conesa EJ, Egea JA, Miguel V, Toledo C, Meseguer-Valdenebro JL (2017) Optimization of geometric parameters in a welded joint through response surface methodology. Constr Build Mater 154:105–114

    Article  Google Scholar 

  10. Koli Y, Yuvaraj N, Aravindan S (2020) Multi-response mathematical modeling for prediction of weld bead geometry of AA6061-T6 using response surface methodology. Trans Indian Inst Metals 73:645–666

    Article  Google Scholar 

  11. Terner M, Bayarsaikhan TA, Hong HU, Lee JH (2017) Influence of gas metal arc welding parameters on the bead properties in automatic cladding. J Weld Join 35:16–25

    Article  Google Scholar 

  12. Kumar S, Singh PK, Dpatel SBP (2017) Optimization of welding parameters of GTAW using response surface methodology. Sci Bull Ser-D 79:119–132

    Google Scholar 

  13. Moghaddam MA, Golmezergi R, Kolahan F (2016) Multi-variable measurements and optimization of GMAW parameters for API-X42 steel alloy using a hybrid BPNN-PSO approach. Measurement 92:279–287

    Article  Google Scholar 

  14. Pal K, Pal SK (2019) Multi-objective optimization of pulsed gas metal arc welding process using neuro NSGA-II. J Inst Eng (India): Ser C 100:501–510

    Google Scholar 

  15. Azadi Moghaddam M, Golmezerji R, Kolahan F (2017) Simultaneous optimization of joint edge geometry and process parameters in gas metal arc welding using integrated ANN-PSO approach. Sci Iran 24:260–273

    Google Scholar 

  16. Sivasakthivel PS, Sudhakaran R (2020) Modelling and optimisation of welding parameters for multiple objectives in pre-heated gas metal arc welding process using nature instigated algorithms. Aust J Mech Eng 18:S76–S87

    Article  Google Scholar 

  17. Ramesh R, Dinaharan I, Kumar R, Akinlabi ET (2017) Microstructure and mechanical characterization of friction stir welded high strength low alloy steels. Mater Sci Eng A 687:39–46

    Article  Google Scholar 

  18. Subrammanian A, Jabaraj DB, Bupesh Raja VK (2015) Investigation of microstructure and mechanical properties of resistance spot welded dissimilar joints between ferritic stainless steel and weathering steel. Appl Mech Mater 766:770–779

    Article  Google Scholar 

  19. John B, Paulraj S, Mathew J (2016) The role of shielding gas on mechanical, metallurgical and corrosion properties of corten steel welded joints of railway coaches using GMAW. Adv Sci Technol Res J 10:156–168

    Article  Google Scholar 

  20. Deepak JR, Raja VB, Arputhabalan JJ, Kumar GY, Thomas SK (2019) Experimental investigation of corten A588 filler rod for welding weathering steel. Mater Today: Proceed 16:1233–1238

    Google Scholar 

  21. Deepak JR, Bupesh Raja VK, Janardhan Guptha M, Durga Prasad PH, Sriram V (2017) Experimental investigation of mechanical properties of welded corten steel A588 grade plate using ER70S-6 filler material for construction application. IOP Confer Ser: Mater Sci Eng 197:012067

    Article  Google Scholar 

  22. Waghmare U, Dhoble AS, Taiwade R, Verma J, Vashishtha H (2019) Prediction of heat affected zone and other mechanical properties of welded joints of HSLA A588-B of jet blast deflector. World J Eng 16:438–444

    Article  Google Scholar 

  23. Sivakumar J, Vasudevan M, Korra NN (2020) Systematic welding process parameter optimization in activated tungsten inert gas (A-TIG) welding of inconel 625. Trans Indian Inst Metals 73:555–569

    Article  Google Scholar 

  24. Jafaryeganeh H, Ventura M, Soares CG (2020) Effect of normalization techniques in multi-criteria decision making methods for the design of ship internal layout from a Pareto optimal set. Struct Multidiscip Optim 62:1849–1863

    Article  MathSciNet  Google Scholar 

  25. Guizani H, Nasser MB, Tlili B, Oueslati A, Chafra M (2019) Finishing and quality of mechanically brushed 316L stainless steel welded joints using MIG process: hardness modeling by L9 TAGUCHI design. Int J Adv Manuf Technol 105:1009–1022

    Article  Google Scholar 

  26. Mvola B, Kah P, Layus P (2018) Review of current waveform control effects on weld geometry in gas metal arc welding process. Int J Adv Manuf Technol 96:4243–4265

    Article  Google Scholar 

  27. Chandrasekaran RR, Benoit MJ, Barrett JM, Gerlich AP (2019) Multi-variable statistical models for predicting bead geometry in gas metal arc welding. Int J Adv Manuf Technol 105:1573–1584

    Article  Google Scholar 

  28. Zhao D, Ivanov M, Wang Y, Liang D, Du W (2020) Multi-objective optimization of the resistance spot welding process using a hybrid approach. J Intell Manuf. https://doi.org/10.1007/s10845-020-01638-2

Download references

Availability of data and material

The data can be provided by the corresponding author under reasonable requirements.

Funding

The authors are grateful for the financial support provided by the Natural Science Foundation of Shandong Province (ZR2016EEM47/ZR2018PEE004) and open projects of State Key Laboratory for Strength and Vibration of Mechanical Structures (SV2019-KF-39).

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Dawei Zhao.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

Code availability

The results in this article can be replicated via software such as Minitab, MODDE, and Design Expert.

Additional information

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Zhao, D., Bezgans, Y., Vdonin, N. et al. The use of TOPSIS-based-desirability function approach to optimize the balances among mechanical performances, energy consumption, and production efficiency of the arc welding process. Int J Adv Manuf Technol 112, 3545–3559 (2021). https://doi.org/10.1007/s00170-021-06601-w

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00170-021-06601-w

Keywords

Navigation