In this paper, Fe-based and Co-based alloy powders were chosen to perform laser cladding on wheel materials through conventional laser cladding (CLC) and ultra-high-speed laser cladding (UHSLC) processes, respectively. The microstructures, element distribution, phase composition and hardness of the Fe-based alloy and Co-based alloy coating layers using the CLC and UHSLC processes were compared and analysed. The results show that the CLC and UHSLC alloy coatings were dense and free of defects such as pores and cracks. Compared with the CLC alloy coating, the grain size of the UHSLC alloy coating was smaller, the coating composition was close to the powder design composition, and the distribution of Cr within and between the grains was more uniform. The Fe-based coating was mainly composed of (Fe, Ni) and Cr₇C₃, and the Co-based coating was mainly composed of γ-Co and Cr₂₃C₆. It was found that the cooling rate of the CLC alloy coating was smaller than that of the USHLC, and the hardness of the CLC alloy coating was less than that of the USHLC. The average hardness of the UHSLC Fe-based and Co-based alloy coatings was 709 HV and 525 HV, respectively. The average hardness of the CLC Fe-based and Co-based alloy coatings was 615 HV and 493 HV, respectively. The rolling friction and wear tests were carried out with the CLC-treated and UHSLC-treated wheel specimens on the GPM-30 rolling contact fatigue testing machine. The results showed that the wear rate of the UHSLC alloy coating on the wheel specimens was significantly lower than that of the CLC alloy coating on the wheel specimens. The wear rates of the UHSLC Fe-based and Co-based alloy coatings on the wheel specimens were reduced by 40.7% and 73.8%, respectively. It was demonstrated that the wear resistance of the USHLC alloy coatings was better than those of the CLC alloy coatings. The CLC alloy coating exhibited more severe fatigue damage with small cracks. Furthermore, the damage of the UHSLC alloy coating was relatively minor, with slight spalling. The Co-based alloy coating exhibited superior wear properties with the same laser cladding process.

Wheels are critical components of railway vehicles, and the dynamic measurement of wheel parameters is of paramount importance for the safe operation of trains.To enhance the matching accuracy in the existing dynamic measurement processes for train wheel parameters, this paper proposes an improved point cloud registration algorithm based on key point fusion of the Super Four-Points Congruent Sets (Super-4PCS) and Iterative Closest Point (ICP) algorithm. Firstly, point cloud filtering and normal estimation are performed on the wheel point cloud data to obtain source and target point clouds with normal information. Subsequently, the Intrinsic Shape Signatures (ISS) algorithm is employed to extract key points, and the Fast Point Feature Histograms (FPFH) point cloud feature descriptor is utilized to characterize the extracted key points. Then, a two-level registration strategy is used to improve registration accuracy, in which the Super-4PCS algorithm is applied for primary coarse registration and the ICP algorithm is used for the secondary fine registration, respectively. Finally, the experiment is conducted to validate the proposed algorithm and the performance of the algorithm is further comparative analyzed through the listed registration evaluation metrics. Experimental results demonstrate that the proposed algorithm significantly improves registration accuracy and robustness for wheel point cloud data, with the Root Mean Square Error (RMSE) reduced from 0.0631 to 0.0002, and the Mean Absolute Error (MAE) reduced from 0.0671 to 0.00026, compared to traditional algorithms. However, the algorithm’s performance is sensitive to point cloud density and noise levels, and its effectiveness may vary under different environmental conditions.

Wheels are critical components of railway vehicles, and the dynamic measurement of wheel parameters is of paramount importance for the safe operation of trains.To enhance the matching accuracy in the existing dynamic measurement processes for train wheel parameters, this paper proposes an improved point cloud registration algorithm based on key point fusion of the Super-4PCS(Super Four-Points Congruent Sets) and ICP (Iterative Closest Point) algorithm. Firstly, point cloud filtering and normal estimation are performed on the wheel point cloud data to obtain source and target point clouds with normal information. Subsequently, the ISS algorithm is employed to extract key points, and the FPFH(Fast Point Feature Histograms) point cloud feature descriptor is utilized to characterize the extracted key points. Then, a two-level registration strategy is used to improve registration accuracy, in which the Super-4PCS algorithm is applied for primary coarse registration and the ICP algorithm is used for the secondary fine registration, respectively. Finally, the experiment is conducted to validate the proposed algorithm and the performance of the algorithm is further comparative analyzed through the listed registration evaluation metrics. Experimental results demonstrate that the proposed algorithm exhibits significant improvements in registration accuracy and robustness for wheel point cloud data compared to the traditional algorithms.