Author Affiliations
1College of Mechanical Engineering, Zhejiang University of Technology, Hangzhou, Zhejiang 310014, China1Institute of Laser Advanced Manufacturing, Zhejiang University of Technology, Hangzhou, Zhejiang 310014, China2Collaborative Innovation Center of High-End Laser Manufacturing Equipment, Hangzhou, Zhejiang 310014, Chinashow less
Fig. 1. Schematic of laser cladding
Fig. 2. Growth schematic of dendrite in solid tip grid
Fig. 3. Grid distribution and boundary conditions of computational domain
Fig. 4. Schematic of computation domain evolution in laser cladding process
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
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
Fig. 7. Schematic of solute redistribution coefficient during non-equilibrium solidification
Fig. 8. Simulated flow field distribution (t=2.1 s). (a) Dynamic solute redistribution coefficient solidification model; (b) constant solute redistribution coefficient solidification model
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
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
Fig. 11. Error between Cr mass fractions simulated by two models and experimental data
Fig. 12. Comparison between Cr mass fractions in x direction simulated by two models and experimental data
Material | Mass fraction /% |
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C | Si | Mn | Cr | Ni | Mo | Fe |
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45 steel | 0.45 | 0.20 | 0.60 | | | | Bal. | 316L stainless steel powder | 0.02 | 0.55 | 1.55 | 16.0 | 10.0 | 2.08 | Bal. |
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Table 1. Chemical composition of 45 steel and 316L stainless steel powder
Parameter | Value |
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Reference density ρref /(kg·m-3) | 8000 | Specific heat cp /(J·kg-1·K-1) | 500 | Melting point Tf /K | 1805.15 | 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 /m | 8.0×10-6 | 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 /nm | 50 |
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Table 2. Thermophysical parameters used in numerical simulation
[22-24] Time /s | Cladding height /mm | Molten pool depth/mm |
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Dynamic solute redistribution coefficient solidification model | Constant solute redistribution coefficient solidification model | Dynamic solute redistribution coefficient solidification model | Constant solute redistribution coefficient solidification model |
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1.7 | 0.378 | 0.375 | 0.736 | 0.746 | 2.1 | 0.380 | 0.386 | 0.724 | 0.737 | 2.5 | 0.382 | 0.378 | 0.728 | 0.743 |
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Table 3. Predicted cladding layer height and molten pool depth by the two model at different time points