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Open AccessOpen Access JUSTC Engineering & Materials 02 May 2023

Multiobjective optimization of morphologies and performance of Q355C gas metal arc welding based on the NSGA-Ⅱ

  • School of Mechanical and Automotive Engineering, Fujian University of Technology, Fuzhou 350118, China

Cite this:
https://doi.org/10.52396/JUSTC-2022-0112
More Information
  • Author Bio:

    Huajing Weng is currently a graduate student at the Fujian University of Technology. Her research mainly focuses on wire and arc additive manufacturing

    Guofu Lian is a Professor at Fujian University of Technology. He received his Ph.D. degree from the University of Science and Technology of China in 2011. His research interests primarily include additive manufacturing and laser surface engineering

  • Corresponding author: E-mail: gflian@mail.ustc.edu.cn
  • Received Date: 10 August 2022
  • Accepted Date: 09 December 2022
    • Available Online: 02 May 2023
    Abstract

  • Abstract

    This work studied the influence law of gas-metal-arc welding process parameters on the morphologies and performance to improve the morphologies and performance. The mixed orthogonal surfacing test was carried out by taking the preheating temperature, welding voltage, current, speed, and wire extension as GMAW process parameters. The aspect ratio decreased with increasing welding voltage, and it first increased and then decreased with increasing welding current. The hardness increased with increasing preheating temperature and welding speed and decreased with increasing welding voltage, current, and wire extension. Residual stress increased with the increased preheating temperature. In addition, it first decreased and then increased with increasing welding voltage and speed. Based on the regression model, the nondominated sorting genetic algorithm II (NSGA-II) was used for multiobjective optimization. After that, experiments were conducted to verify the noninferior solutions among the aspect ratio, hardness, and residual stress. Errors between the predicted and experimental results by the three output indices were all less than 10%, indicating the feasibility of the optimization method. The research results provide a theoretical direction for multiobjective optimization and refined applications of arc welding.

    Graphical abstract

    Multi-objective optimization of weld seam.

    Abstract

    This work studied the influence law of gas-metal-arc welding process parameters on the morphologies and performance to improve the morphologies and performance. The mixed orthogonal surfacing test was carried out by taking the preheating temperature, welding voltage, current, speed, and wire extension as GMAW process parameters. The aspect ratio decreased with increasing welding voltage, and it first increased and then decreased with increasing welding current. The hardness increased with increasing preheating temperature and welding speed and decreased with increasing welding voltage, current, and wire extension. Residual stress increased with the increased preheating temperature. In addition, it first decreased and then increased with increasing welding voltage and speed. Based on the regression model, the nondominated sorting genetic algorithm II (NSGA-II) was used for multiobjective optimization. After that, experiments were conducted to verify the noninferior solutions among the aspect ratio, hardness, and residual stress. Errors between the predicted and experimental results by the three output indices were all less than 10%, indicating the feasibility of the optimization method. The research results provide a theoretical direction for multiobjective optimization and refined applications of arc welding.

    • Public Summary

    • This work studied the influence law of gas-metal-arc welding process parameters on the morphologies and performance to improve morphologies and performance.
    • The significance analysis shows that the welding voltage and welding current significantly affect the aspect ratio.
    • Based on the significance analysis, hardness is significantly affected by the preheating temperature, welding voltage, current, speed, and wire extension. The preheating temperature, welding voltage, and welding speed significantly affect residual stress.
    • The welding parameters were selected from the Pareto optimal frontier according to actual industrial requirements.

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    • References(24)
  • [1]
    Thompson Martínez R, Alvarez Bestard G, Martins Almeida Silva A, et al. Analysis of GMAW process with deep learning and machine learning techniques. Journal of Manufacturing Processes, 2021, 62: 695–703. doi: 10.1016/j.jmapro.2020.12.052
    [2]
    Ramarao M, Francis Luther King M, Sivakumar A, et al. Optimizing GMAW parameters to achieve high impact strength of the dissimilar weld joints using Taguchi approach. Materials Today:Proceedings, 2022, 50: 861–866. doi: 10.1016/j.matpr.2021.06.137
    [3]
    Wu K, Zeng Y, Zhang M, et al. Effect of high-frequency phase shift on metal transfer and weld formation in aluminum alloy double-wire DP-GMAW. Journal of Manufacturing Processes, 2022, 75: 301–319. doi: 10.1016/j.jmapro.2021.12.005
    [4]
    Ahn J, Chen L, Davies C M, et al. Parametric optimisation and microstructural analysis on high power Yb-fibre laser welding of Ti-6Al-4V. Optics and Lasers in Engineering, 2016, 86: 156–171. doi: 10.1016/j.optlaseng.2016.06.002
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    Lian G, Xiao S, Zhang Y, et al. Multi-objective optimization of coating properties and cladding efficiency in 316L/WC composite laser cladding based on grey relational analysis. The International Journal of Advanced Manufacturing Technology, 2021, 112: 1449–1459. doi: 10.1007/s00170-020-06486-1
    [12]
    Weng H, Jiang J, Feng M, et al. Multi-objective optimizations of the Q355C steel gas metal arc welding process based on the grey correlation analysis. The International Journal of Advanced Manufacturing Technology, 2022, 121: 3567–3582. doi: 10.1007/s00170-022-09547-9
    [13]
    Devendranath Ramkumar K, Mishra D, Ganesh Raj B, et al. Effect of optimal weld parameters in the microstructure and mechanical properties of autogeneous gas tungsten arc weldments of super-duplex stainless steel UNS S32750. Materials & Design (1980-2015), 2015, 66: 356–365. doi: 10.1016/j.matdes.2014.10.084
    [14]
    Hu C, Chen L, Zhang X, et al. Effects of preheating-induced interlaminar microstructural evolution on performance of fiber laser welded high strength low alloy steel. Journal of Materials Research and Technology, 2022, 16: 335–346. doi: 10.1016/j.jmrt.2021.12.010
    [15]
    Chaudhary V, Bharti A, Azam S M, et al. A re-investigation: Effect of TIG welding parameters on microstructure, mechanical, corrosion properties of welded joints. Materials Today: Proceedings, 2021, 45: 4575–4580. doi: 10.1016/j.matpr.2021.01.007
    [16]
    Cevik B, Koç M. The effects of welding speed on the microstructure and mechanical properties of marine-grade aluminium (AA5754) alloy welded using MIG welding. Metallic Materials, 2021, 57: 307–316. doi: 10.4149/km_2019_5_307
    [17]
    Roy J G, Yuvaraj N, Vipin. Effect of welding parameters on mechanical properties of cold metal transfer welded thin AISI 304 stainless-steel sheets. Transactions of the Indian Institute of Metals, 2021, 74: 2397–2408. doi: 10.1007/s12666-021-02326-2
    [18]
    Kumar B, Bag S, Mahadevan S, et al. On the interaction of microstructural morphology with residual stress in fiber laser welding of austenitic stainless steel. CIRP Journal of Manufacturing Science and Technology, 2021, 33: 158–175. doi: 10.1016/j.cirpj.2021.03.009
    [19]
    Kumar S, Shahi A S. Effect of heat input on the microstructure and mechanical properties of gas tungsten arc welded AISI 304 stainless steel joints. Materials & Design, 2011, 32: 3617–3623. doi: 10.1016/j.matdes.2011.02.017
    [20]
    Sokolov M, Salminen A, Kuznetsov M, et al. Laser welding and weld hardness analysis of thick section S355 structural steel. Materials & Design, 2011, 32 (10): 5127–5131. doi: 10.1016/j.matdes.2011.05.053
    [21]
    Sahu M, Paul A, Ganguly S. Optimization of process parameters of friction stir welded joints of marine grade AA 5083. Materials Today: Proceedings, 2021, 44: 2957–2962. doi: 10.1016/j.matpr.2021.01.938
    [22]
    Han L, Han T, Chen G, et al. Influence of heat input on microstructure, hardness and pitting corrosion of weld metal in duplex stainless steel welded by keyhole-TIG. Materials Characterization, 2021, 175: 111052. doi: 10.1016/j.matchar.2021.111052
    [23]
    Yürük A, Çevik B, Kahraman N. Analysis of mechanical and microstructural properties of gas metal arc welded dissimilar aluminum alloys (AA5754/AA6013). Materials Chemistry and Physics, 2021, 273: 125117. doi: 10.1016/j.matchemphys.2021.125117
    [24]
    Hu X D, Wang J T, Yang Y C, et al. 304/Q345R composite plate welded joint microstructure and residual stress. Transactions of the China Welding Institution, 2020, 41 (7): 39–45. doi: 10.12073/j.hjxb.20190915001
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    Figure  1.  GMAW welding system.

    Figure  2.  Schematic cross-section diagram of the weld joint.

    Figure  3.  Main effects of (a) the aspect ratio model, (b) the hardness model, and (c) the residual stress model.

    Figure  4.  Surface plots of (a) the hardness, I, and T ; (b) the hardness, U, and I; (c) the hardness, I, and S; (d) the hardness, I, and L.

    Figure  5.  Surface plots of (a) the residual stress, T, and U; (b) the residual stress, T, and S.

    Figure  6.  (a) NGSA-Ⅱ flow chart, (b) Pareto frontier diagram, (c) Pareto optimal surface, and (d) contour plots.

    Figure  7.  Cross-sectional morphology of verification experiment.

    Figure  8.  Microstructure morphology and energy spectrum of welded joints (a) weld, (b) heat affected zone, and (c) base metal.

    [1]
    Thompson Martínez R, Alvarez Bestard G, Martins Almeida Silva A, et al. Analysis of GMAW process with deep learning and machine learning techniques. Journal of Manufacturing Processes, 2021, 62: 695–703. doi: 10.1016/j.jmapro.2020.12.052
    [2]
    Ramarao M, Francis Luther King M, Sivakumar A, et al. Optimizing GMAW parameters to achieve high impact strength of the dissimilar weld joints using Taguchi approach. Materials Today:Proceedings, 2022, 50: 861–866. doi: 10.1016/j.matpr.2021.06.137
    [3]
    Wu K, Zeng Y, Zhang M, et al. Effect of high-frequency phase shift on metal transfer and weld formation in aluminum alloy double-wire DP-GMAW. Journal of Manufacturing Processes, 2022, 75: 301–319. doi: 10.1016/j.jmapro.2021.12.005
    [4]
    Ahn J, Chen L, Davies C M, et al. Parametric optimisation and microstructural analysis on high power Yb-fibre laser welding of Ti-6Al-4V. Optics and Lasers in Engineering, 2016, 86: 156–171. doi: 10.1016/j.optlaseng.2016.06.002
    [5]
    Sivakumar J, Vasudevan M, Korra N N. Systematic welding process parameter optimization in activated tungsten inert gas (A-TIG) welding of Inconel 625. Transactions of the Indian Institute of Metals, 2020, 73: 555–569. doi: 10.1007/s12666-020-01876-1
    [6]
    Meena S L, Butola R, Murtaza Q et al. Metallurgical investigations of microstructure and micro hardness across the various zones in synergic MIG welding of stainless steel. Materials Today: Proceedings, 2017, 4 (8): 8240–8249. doi: 10.1016/j.matpr.2017.07.166
    [7]
    Mishra P, Pandit M, Sood S, et al. Development of mathematical model for prediction and control of weld dilution in MIG welded stainless steel 202 plates. In: Govindan K, Kumar H, Yadav S, editors. Advances in Mechanical and Materials Technology. Singapore: Springer, 2022: 431–443.
    [8]
    Ghosh N, Pal P K, Nandi G. Parametric optimization of MIG welding on 316L austenitic stainless steel by grey-based Taguchi method. Procedia Technology, 2016, 25: 1038–1048. doi: 10.1016/j.protcy.2016.08.204
    [9]
    Rubio-Ramirez C, Giarollo D F, Mazzaferro J E, et al. Prediction of angular distortion due GMAW process of thin-sheets Hardox 450® steel by numerical model and artificial neural network. Journal of Manufacturing Processes, 2021, 68: 1202–1213. doi: 10.1016/j.jmapro.2021.06.045
    [10]
    Lian G, Zhang H, Zhang Y, et al. Optimizing processing parameters for multi-track laser cladding utilizing multi-response grey relational analysis. Coatings, 2019, 9 (6): 356. doi: 10.3390/coatings9060356
    [11]
    Lian G, Xiao S, Zhang Y, et al. Multi-objective optimization of coating properties and cladding efficiency in 316L/WC composite laser cladding based on grey relational analysis. The International Journal of Advanced Manufacturing Technology, 2021, 112: 1449–1459. doi: 10.1007/s00170-020-06486-1
    [12]
    Weng H, Jiang J, Feng M, et al. Multi-objective optimizations of the Q355C steel gas metal arc welding process based on the grey correlation analysis. The International Journal of Advanced Manufacturing Technology, 2022, 121: 3567–3582. doi: 10.1007/s00170-022-09547-9
    [13]
    Devendranath Ramkumar K, Mishra D, Ganesh Raj B, et al. Effect of optimal weld parameters in the microstructure and mechanical properties of autogeneous gas tungsten arc weldments of super-duplex stainless steel UNS S32750. Materials & Design (1980-2015), 2015, 66: 356–365. doi: 10.1016/j.matdes.2014.10.084
    [14]
    Hu C, Chen L, Zhang X, et al. Effects of preheating-induced interlaminar microstructural evolution on performance of fiber laser welded high strength low alloy steel. Journal of Materials Research and Technology, 2022, 16: 335–346. doi: 10.1016/j.jmrt.2021.12.010
    [15]
    Chaudhary V, Bharti A, Azam S M, et al. A re-investigation: Effect of TIG welding parameters on microstructure, mechanical, corrosion properties of welded joints. Materials Today: Proceedings, 2021, 45: 4575–4580. doi: 10.1016/j.matpr.2021.01.007
    [16]
    Cevik B, Koç M. The effects of welding speed on the microstructure and mechanical properties of marine-grade aluminium (AA5754) alloy welded using MIG welding. Metallic Materials, 2021, 57: 307–316. doi: 10.4149/km_2019_5_307
    [17]
    Roy J G, Yuvaraj N, Vipin. Effect of welding parameters on mechanical properties of cold metal transfer welded thin AISI 304 stainless-steel sheets. Transactions of the Indian Institute of Metals, 2021, 74: 2397–2408. doi: 10.1007/s12666-021-02326-2
    [18]
    Kumar B, Bag S, Mahadevan S, et al. On the interaction of microstructural morphology with residual stress in fiber laser welding of austenitic stainless steel. CIRP Journal of Manufacturing Science and Technology, 2021, 33: 158–175. doi: 10.1016/j.cirpj.2021.03.009
    [19]
    Kumar S, Shahi A S. Effect of heat input on the microstructure and mechanical properties of gas tungsten arc welded AISI 304 stainless steel joints. Materials & Design, 2011, 32: 3617–3623. doi: 10.1016/j.matdes.2011.02.017
    [20]
    Sokolov M, Salminen A, Kuznetsov M, et al. Laser welding and weld hardness analysis of thick section S355 structural steel. Materials & Design, 2011, 32 (10): 5127–5131. doi: 10.1016/j.matdes.2011.05.053
    [21]
    Sahu M, Paul A, Ganguly S. Optimization of process parameters of friction stir welded joints of marine grade AA 5083. Materials Today: Proceedings, 2021, 44: 2957–2962. doi: 10.1016/j.matpr.2021.01.938
    [22]
    Han L, Han T, Chen G, et al. Influence of heat input on microstructure, hardness and pitting corrosion of weld metal in duplex stainless steel welded by keyhole-TIG. Materials Characterization, 2021, 175: 111052. doi: 10.1016/j.matchar.2021.111052
    [23]
    Yürük A, Çevik B, Kahraman N. Analysis of mechanical and microstructural properties of gas metal arc welded dissimilar aluminum alloys (AA5754/AA6013). Materials Chemistry and Physics, 2021, 273: 125117. doi: 10.1016/j.matchemphys.2021.125117
    [24]
    Hu X D, Wang J T, Yang Y C, et al. 304/Q345R composite plate welded joint microstructure and residual stress. Transactions of the China Welding Institution, 2020, 41 (7): 39–45. doi: 10.12073/j.hjxb.20190915001
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