Yue Yang

Automatic ultrasonic inspection of flaws in a propeller-blade

Ultrasonic technique is very promising for non destructive inspection. In this paper, a method is presented on automatic ultrasonic inspection of defects in a propeller-blade without computer aided design (CAD) models. The 3D surface data are obtained by ultrasonic measurement, and then the inspection path is planned after the CAD model has been reconstructed. A C-scan image is obtained in real-time ultrasonic automatic inspection. Thereafter, defective area and sound area are separated through binarisation of the C-scan image, and an auxiliary table is used to segment defects in order that defects are disconnected to each other. Then, an algorithm based on edge element is proposed, simplifying the process of extracting edge. Finally, application of these procedures for inspecting a propeller-blade is demonstrated.

CF-Kriging surrogate model based on the combination forecasting method:

The spatial correlation function (SCF) is an important part of the Kriging surrogate model that describes the sample data structure, and the SCF affects the fitting accuracy of the Kriging surrogate model directly. In a Kriging surrogate model, a single SCF is typically selected to describe the sample data structure, which may cause sample information loss and fitting error. A new Kriging surrogate model for combination forecasting (CF-Kriging) was constructed by integrating of linear weighted approach based on the combination forecasting method, in which the differences in the sample information described by the diverse SCFs for the same sample data structure were considered. The integrity of the sample information of the CF-Kriging model was improved using single-SCF Kriging surrogate models as sub-models and considering the minimum mean absolute percentage error as the improved target for the fitting accuracy. The effectiveness of the CF-Kriging surrogate model was demonstrated using four test functions and two engineering problems, which indicated that the CF-Kriging surrogate model could effectively improve the fitting accuracy and the fitting stability of an ordinary Kriging surrogate model.

Optimization of the suspension parameters of a rail vehicle based on a virtual prototype Kriging surrogate model

The design and integration of suspension parameters directly affects the riding quality of a rail vehicle. This study is intended to develop an approach to the optimization of suspension parameters of rail vehicles based on a virtual prototype Kriging model. To construct the virtual prototype Kriging model, a virtual prototype model of a rail vehicle and its suspension system was established based on a vertical model for its dynamics and using virtual prototype software. A virtual prototype Kriging model of a rail vehicle based on riding quality was also proposed, in which the training sample was obtained as different combinations of suspension parameters using the virtual prototype and dynamics simulations based on the design of experiments method. On this basis, an optimization model of the suspension parameters was established, in which the objective function was the Kriging model of the riding quality index. The optimized combination of suspension parameters was determined using the Multi-Island Genetic Algorithm. The dynamics simulation results before and after optimization for different rail profiles indicated that the riding quality was significantly improved, which demonstrated the universality and effectiveness of this approach.

Heterogeneous model integration of complex mechanical parts based on semantic feature fusion

Due to the rapid development of computer, sensor, and automatic control technologies, the amount of data generated during product design and manufacturing is increasing significantly. The product data bank is large, complex, heterogeneous, and often fast-changing; it is difficult to integrate heterogeneous models using the conventional method. Therefore, a semantic feature fusion-based heterogeneous model integration method is proposed. First, the error in the geometric dimensions and position are extracted using model registration. Second, the basic geometric feature is obtained using slippage analysis. Third, the extracted data, such as the basic geometric feature and the error in the geometric dimensions and position, are fused into the design model using the level set method. Finally, the marching cubes method is introduced to reconstruct the surface of the fused model. The empirical results demonstrate that the proposed algorithm can integrate all types of semantic features and geometric features into a basic product model effectively and efficiently.

Optimization of the target profile for asymmetrical rail grinding in sharp-radius curves for high-speed railways

Asymmetrical rail grinding in sharp-radius curves could reduce the side wear of railheads and enhance curve capacity of rail vehicles. The wheel/rail contact performance and curve capacity could be further improved by the optimization of the asymmetrical rail grinding target profile. In order to modify the target profile smoothly, the nonuniform rational B-spline curve with adjustable weight factors is used to establish a parameterized model of railhead curves in the asymmetrical grinding region. The indices of contact performance and curve capacity for different weight factors are obtained using experiment design and service performance simulation. Two Kriging surrogate models are proposed, in which the design variables are the adjustable weight factors, and the response parameters are the indices of contact performance and curve capacity, respectively. The multi-objective optimization model of the target profile is established, in which the objective functions are the two Kriging surrogate models of contact performance and curve capacity. The optimized weight factors are sought using a nondominated sorting genetic algorithm II, and the corresponding optimal target profile is obtained. The wheel/rail service performance simulation before and after optimization indicates that the contact performance and curve capacity are improved significantly.