# Friction and Wear Performances of Materials for Wind Turbine Sliding Bearing Bushes
## 3 Wind Turbine Sliding Bearing Bearing Bush Coating Preparation  
### Numerical Calculation of Laser Cladding Power Parameters  
The temperature field generated during laser cladding was numerically simulated using ANSYS software(Version 2022) based on the finite element method to determine the appropriate laser power parameters and enhance the forming quality of the cladding parts. The transient thermal changes of cladding parts during processing were analyzed [22; 23; 24]. Figure 1: Schematic diagram of the principle of laser cladding. Figure 2: MFT5000 Multi-function friction and wear testing machine. In the numerical simulation, the laser energy density follows an approximately Gaussian distribution. Therefore, a Gaussian heat source model was chosen for simulation [25; 26]. As illustrated in Figure 3, activities included establishing the finite element model, grid division, node setup and arranging the movement trajectory of the heat source. The dimensions of the substrate (length \(\times\) width \(\times\) thickness) were set at 50 mm \(\times\) 50 mm \(\times\) 10 mm, and the coating's dimensions (length \(\times\) radius of the semi-cylinder) were 50 mm \(\times\) 2 mm. The mesh cell type selected was the highly flexible 8-node 6-hedron. Position grids near the coating were refined, resulting in a total of 4522 grids. This refinement ensured uniform heat transfer and calculation accuracy while reducing calculation time. Laser cladding was performed in the direction shown in Figure 3.  
The laser Gaussian heat source, dynamic boundary conditions and material thermal and physical parameters were imposed to the 3D numerical model. Due to the large temperature span during the laser cladding process, if the thermal and physical properties of the material are considered to be independent of the temperature, it will affect the calculation results. Therefore, the relevant thermal and physical parameters used in this paper were calculated by JMatPro software(Version 7.0) according to the elemental composition of the material, as shown in Figure 4.Under scanning speed conditions of 6 mm/s, powder feeding capacity of 1.6 r/min, and defocusing capacity of 16 mm, a CoCrFeNiMo high-entropy alloy coating was prepared on the surface of 9 Cr18 bearing steel with a power of 1300 W, while a Ni-Cr-Mo-Si alloy coating was prepared on the surface of MTCrMoCu30 wear-resistant cast iron with a power of 1000 W. As shown in Figure 3, five temperature acquisition points are set up on the cladding layer with a gradient of 5 mm to capture the change rule of nodal temperature with time. The temperature field cloud map at any point in the numerical simulation process and the temperature-time history change curves of different nodes are depicted in Figure 5. The elliptical temperature field is a result of the laser heat source focusing on a node, causing a sharp temperature increase, and as the laser heat source moves away, the temperature at the point decreases rapidly. Molten pools melt and solidify at different rates, necessitating a cooling period. The temperature change trend of different nodes with time is similar, reflecting the laser cladding processing characteristics of fast cooling and heating. However, the cooling rate is less than the heating rate, a phenomenon associated with the release of latent crystallization heat during the solidification process. The maximum temperature in the melting pool of the CoCrFeNiMo high-entropy alloy coating can reach 2221 \({}^{\circ}\)C, surpassing the melting point of 9 Cr18 bearing steel (1371 \({}^{\circ}\)C) and CoCrFeNiMo high-entropy alloy(1350 \({}^{\circ}\)C). The highest temperature in the Ni-Cr-Mo-Si alloy coating pool can reach 2100 \({}^{\circ}\)C, exceeding the melting point of MTCrMoCu30 wear-resistant cast iron (1400 \({}^{\circ}\)C) and Ni-Cr-Mo-Si alloy (1420 \({}^{\circ}\)C). Both material groups successfully achieve the cladding effect, forming a robust metallurgical combination. Figure 3: The 3D numerical model. Figure 4: Thermal property parameters of each material.