• Chinese Journal of Lasers
  • Vol. 49, Issue 2, 0202012 (2022)
Caowei Zhang1、1、2, Honghao Ge1、1、2、*, Hao Fang1、1、2, Qunli Zhang1、1、2, and Jianhua Yao1、1、2
Author Affiliations
  • 1College of Mechanical Engineering, Zhejiang University of Technology, Hangzhou, Zhejiang 310014, China
  • 1Institute of Laser Advanced Manufacturing, Zhejiang University of Technology, Hangzhou, Zhejiang 310014, China
  • 2Collaborative Innovation Center of High-End Laser Manufacturing Equipment, Hangzhou, Zhejiang 310014, China
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    DOI: 10.3788/CJL202249.0202012 Cite this Article Set citation alerts
    Caowei Zhang, Honghao Ge, Hao Fang, Qunli Zhang, Jianhua Yao. Effect of Solute Redistribution Coefficient on Solute Distribution in Laser Cladding[J]. Chinese Journal of Lasers, 2022, 49(2): 0202012 Copy Citation Text show less
    Schematic of laser cladding
    Fig. 1. Schematic of laser cladding
    Growth schematic of dendrite in solid tip grid
    Fig. 2. Growth schematic of dendrite in solid tip grid
    Grid distribution and boundary conditions of computational domain
    Fig. 3. Grid distribution and boundary conditions of computational domain
    Schematic of computation domain evolution in laser cladding process
    Fig. 4. Schematic of computation domain evolution in laser cladding process
    Evolution of temperature field during laser cladding. (a)(c) Dynamic solute redistribution coefficient solidification model; (d)(f) constant solute redistribution coefficient solidification model
    Fig. 5. Evolution of temperature field during laser cladding. (a)(c) Dynamic solute redistribution coefficient solidification model; (d)(f) constant solute redistribution coefficient solidification model
    Molten pool morphologies during laser cladding (t=2.1 s). (a) Dynamic solute redistribution coefficient solidification model; (b) constant solute redistribution coefficient solidification model; (c) experimental molten pool morphology
    Fig. 6. Molten pool morphologies during laser cladding (t=2.1 s). (a) Dynamic solute redistribution coefficient solidification model; (b) constant solute redistribution coefficient solidification model; (c) experimental molten pool morphology
    Schematic of solute redistribution coefficient during non-equilibrium solidification
    Fig. 7. Schematic of solute redistribution coefficient during non-equilibrium solidification
    Simulated flow field distribution (t=2.1 s). (a) Dynamic solute redistribution coefficient solidification model; (b) constant solute redistribution coefficient solidification model
    Fig. 8. Simulated flow field distribution (t=2.1 s). (a) Dynamic solute redistribution coefficient solidification model; (b) constant solute redistribution coefficient solidification model
    Simulated Cr element distribution. (a) Dynamic solute redistribution coefficient solidification model; (b) constant solute redistribution coefficient solidification model; (c) metallographic microstructure of samples; (d) point locations in y direction for EDS analysis; (e) point locations in x direction for EDS analysis
    Fig. 9. Simulated Cr element distribution. (a) Dynamic solute redistribution coefficient solidification model; (b) constant solute redistribution coefficient solidification model; (c) metallographic microstructure of samples; (d) point   locations in y direction for EDS analysis; (e) point locations in x direction for EDS analysis
    Comparison of simulated and experimental Cr element distributions in y direction. (a) Dynamic solute redistribution coefficient solidification model; (b) constant solute redistribution coefficient solidification model
    Fig. 10. Comparison of simulated and experimental Cr element distributions in y direction. (a) Dynamic solute redistribution coefficient solidification model; (b) constant solute redistribution coefficient solidification model
    Error between Cr mass fractions simulated by two models and experimental data
    Fig. 11. Error between Cr mass fractions simulated by two models and experimental data
    Comparison between Cr mass fractions in x direction simulated by two models and experimental data
    Fig. 12. Comparison between Cr mass fractions in x direction simulated by two models and experimental data
    MaterialMass fraction /%
    CSiMnCrNiMoFe
    45 steel0.450.200.60Bal.
    316L stainless steel powder0.020.551.5516.010.02.08Bal.
    Table 1. Chemical composition of 45 steel and 316L stainless steel powder
    ParameterValue
    Reference density ρref /(kg·m-3)8000
    Specific heat cp /(J·kg-1·K-1)500
    Melting point Tf /K1805.15
    Thermal conductivity of solid ks /(W·m-1·K-1)19.2
    Effective thermal conductivity of liquid kl /(W·m-1·K-1)209.2
    Latent heat Δhf /(J·kg-1)250000
    Viscosity μl /(kg·m-1·s-1)0.0042
    Primary dendrite arm spacing λl /m8.0×10-6
    Volume heat-transfer coefficient H* /(W·m-2·K-1)1.0×109
    Temperature coefficient of surface tension ∂γ/∂T /(N·m-1·K-1)-4.3×10-4
    Equilibrium partition coefficient ke /0.86
    Interfacial diffusion coefficient Di /(m2·s-1)3.0×10-9
    Solid-liquid interface width δi /nm50
    Table 2. Thermophysical parameters used in numerical simulation[22-24]
    Time /sCladding height /mmMolten pool depth/mm
    Dynamic solute redistribution coefficient solidification modelConstant solute redistribution coefficient solidification modelDynamic solute redistribution coefficient solidification modelConstant solute redistribution coefficient solidification model
    1.70.3780.3750.7360.746
    2.10.3800.3860.7240.737
    2.50.3820.3780.7280.743
    Table 3. Predicted cladding layer height and molten pool depth by the two model at different time points
    Caowei Zhang, Honghao Ge, Hao Fang, Qunli Zhang, Jianhua Yao. Effect of Solute Redistribution Coefficient on Solute Distribution in Laser Cladding[J]. Chinese Journal of Lasers, 2022, 49(2): 0202012
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