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Laser & Optoelectronics Progress, Volume. 59, Issue 7, 0714004(2022)

Multi-Objective Optimization of Laser Cladding Parameters Based on RSM and NSGA-Ⅱ Algorithm

Yanyan Wang1、*, Jiahao Li1, Linsen Shu1,2, and Chengming Su3
Author Affiliations
  • 1School of Mechanical Engineering, Shaanxi University of Technology, Hanzhong , Shaanxi 723001, China
  • 2Shaanxi Provincial Key Laboratory of Industrial Automation, Hanzhong , Shaanxi 723001, China
  • 3Shaanxi Tianyuan Intelligent Remanufacturing Co., Ltd., Xi'an , Shaanxi 710065, China
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    AbstractGet PDF(in Chinese)
    Figures&Tables (10)
    Equations (3)
    References (19)
    Cited By (7)
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    Figures & Tables(10)
    3 kW fiber cladding laser

    Fig. 1. 3 kW fiber cladding laser

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    Powder topography

    Fig. 2. Powder topography

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    Cross-section diagram of single cladding layer

    Fig. 3. Cross-section diagram of single cladding layer

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    Residuals for each response value. (a) Dilution rate; (b) HAZ depth; (c) microhardness

    Fig. 4. Residuals for each response value. (a) Dilution rate; (b) HAZ depth; (c) microhardness

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    Flow chart of NSGA-Ⅱ algorithm

    Fig. 5. Flow chart of NSGA-Ⅱ algorithm

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    Pareto front solution after optimization

    Fig. 6. Pareto front solution after optimization

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    Comparison of cross-section morphology of cladding layer between best process parameter group and contrast experimental groups

    Fig. 7. Comparison of cross-section morphology of cladding layer between best process parameter group and contrast experimental groups

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    Comparison of response values between best process parameter group and contrast experimental groups

    Fig. 8. Comparison of response values between best process parameter group and contrast experimental groups

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    • Table 1. Input variable and its response values of BBD experimental design

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      Table 1. Input variable and its response values of BBD experimental design

      BBD experimental numberInput variableResponse value
      Laser power /WScanning speed /（mm·s-1）Powder feeding rate /（r·min-1）Dilution rate /%HAZ depth /μmMicrohardness /HV0.5
      S11800241.544.7299.37623.8
      S21800201.541.2308.75641.6
      S31800202.529.1293.91641.2
      S41800242.026.9286.45654.0
      S51800161.541.7331.07744.3
      S62100162.526.1350.98664.9
      S72100202.035.2312.28415.1
      S82100242.029.7273.49671.0
      S92100162.523.5364.99704.1
      S102100241.542.1307.11627.7
      S112100242.525.9285.17618.5
      S122100202.527.0315.57663.1
      S132100201.540.9333.01748.7
      S142400201.539.7318.64649.3
      S152400242.033.9311.43480.1
      S162400162.024.7409.78631.1
      S172400202.521.6332.48744.7

    • Table 2. Analysis of variance of response values

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      Table 2. Analysis of variance of response values

      Variance sourceResponse value
      Dilution rateHAZ depthMicrohardness
      Model2383.0015756.752922.91
      F value41.936.8446.84
      P value<0.0001<0.0001<0.0001
      Lack of fit0.49260.42310.4625
      R20.98180.99790.9741
      Signal to noise ratio17.5409.54818.840
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    Yanyan Wang, Jiahao Li, Linsen Shu, Chengming Su. Multi-Objective Optimization of Laser Cladding Parameters Based on RSM and NSGA-Ⅱ Algorithm[J]. Laser & Optoelectronics Progress, 2022, 59(7): 0714004

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    Paper Information

    Category: Lasers and Laser Optics

    Received: Jun. 1, 2021

    Accepted: Jun. 23, 2021

    Published Online: Mar. 8, 2022

    The Author Email: Yanyan Wang (wangyanyanustb@163.com)

    DOI:10.3788/LOP202259.0714004

    Topics

    laser devices and laser physics
    Lasers and Laser Optics
    Laser physics
    laser manufacturing
    Instrumentation, Measurement and Metrology