Early detection of defects inside a rail is of great significance to ensure the safety of rail transit. This work investigated the ability of ultrasonic guided waves (UGWs) to detect internal defects in a rail head. First, the model of UGW propagation in rail, which has an irregular cross-section, was constructed based on the semi-analytical finite element (SAFE) method. Fundamental characteristics, such as wavenumber, phase or group velocity, and wave structure inside the rail, were then calculated. Following modal and vibration energy distribution analysis, a guided wave mode that is sensitive to transverse fissure (TF) defects was selected, and its excitation method was proposed. The effectiveness of the excitation method was confirmed by simulations performed in the ABAQUS software. According to the simulation data, the dispersion curve calculated by using the two-dimensional Fourier fast transform (2D-FFT) coincided well with that of the SAFE method. After that, the sensitivity of the selected mode to internal rail defects was validated and its ability to locate defects was also demonstrated. Finally, the effects of excitation frequency, defect size, and vertical and horizontal defect depth on the reflection waveforms were investigated.
Radio Frequency Identification (RFID) technology that is used in pervasive computing environments is widely concerned in recent years. At present, RFID technology is mainly applied in logistics, manufacturing, public service industry and so on. Based on the characteristics of pervasive computing environment, in this paper, a RFID context-aware service model is proposed. The model combines Ontology Language OWL with CC/PP and FOAF specifications to build a context model. Context ontology includes domain ontology and RFID tags ontology. On this basis, the context reasoning and interpretation mechanism based on ontology and rule is introduced. Through the rewritable RFID tag memory, dynamic management of the context in RFID system is realized.Finally, examples are used to demonstrate the feasibility of the model.
Early detection of defects inside the rail is of great significance to ensure the safety of rail transit. This work investigates the ability of ultrasonic guided waves (UGWs) to detect internal defects of the rail head. First, the model of UGWs propagation in rail, which has an irregular cross-section, is constructed based on semi-analytical finite element (SAFE). Fundamental characteristics such as wavenumber, phase or group velocity, and wave structure inside the rail are then calculated. Following modal and vibration energy distribution analysis, a guided wave mode sensitive to transverse fissure (TF) defects is selected and its excitation method is proposed. The effectiveness of the excitation method is confirmed by simulations performed in the ABAQUS software. According to the simulation data, the dispersion curve calculated by using the two-dimensional Fourier Fast Transform (2D-FFT) coincides well with that of SAFE. After that, the sensitivity of the selected mode to rail internal defect have been validated and its ability to locate defects has also been demonstrated. Finally, the effect of excitation frequency, defect size, defect vertical and horizontal depth on the reflection waveforms is investigated.
Based on statistics theory and contact theory, a material removal model for predicting polishing quality in chemical-mechanical polishing (CMP) of multi-material surface is developed. The contacts of pad-workpiece and pad-particle-workpiece are characterized by the elastic–plastic contact mechanism. The material removal is considered to be the sum of the contributions from two movement modes of particles. Within the model, linear material removal volume (LMRV) can be determined as a function of the interactions of workpiece, pad and slurry. The model also determines the relationship between LMRV and roughness. It is found that polishing pad and parameters of polishing conditions have significant effects on the polishing quality. The experimental results show that chemical reagents contribute quite differently to multi-material end-face planarization. The proposed model reveals insights into improvement of the polishing quality of multi-material surface such as fiber array end-face.
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