• Laser & Optoelectronics Progress
  • Vol. 59, Issue 7, 0714004 (2022)
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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    DOI: 10.3788/LOP202259.0714004 Cite this Article Set citation alerts
    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 Copy Citation Text show less
    3 kW fiber cladding laser
    Fig. 1. 3 kW fiber cladding laser
    Powder topography
    Fig. 2. Powder topography
    Cross-section diagram of single cladding layer
    Fig. 3. Cross-section diagram of single cladding layer
    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
    Flow chart of NSGA-Ⅱ algorithm
    Fig. 5. Flow chart of NSGA-Ⅱ algorithm
    Pareto front solution after optimization
    Fig. 6. Pareto front solution after optimization
    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
    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
    BBD experimental numberInput variableResponse value
    Laser power /WScanning speed /(mm·s-1Powder feeding rate /(r·min-1Dilution 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 1. Input variable and its response values of BBD experimental design
    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
    Table 2. Analysis of variance of response values
    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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