Sapphire has been the most widely used substrate material in LEDs, and the demand for non-C-planes crystal is increasing. In this paper, four crystal planes of the A-, C-, M- and R-plane were selected as the research objects. Nanoindentation technology and chemical mechanical polishing technology were used to study the effect of anisotropy on material properties and processing results. The consequence showed that the C-plane was the easiest crystal plane to process with the material removal rate of 5.93 nm/min, while the R-plane was the most difficult with the material removal rate of 2.47 nm/min. Moreover, the research results have great guiding significance for the processing of sapphire with different crystal orientations.
Double-sides polishing technology has the advantages of high flatness and parallelism, and high polishing efficiency. It is the preferred polishing method for the preparation of ultra-thin sapphire wafer. However, the clamping method is a fundamental problem which is currently difficult to solve. In this paper, a layer stacked clamping (LSC) method of ultra-thin sapphire wafer which was used on double-sides processing was proposed and the clamping mechanism of layer stacked clamping (LSC) was studied. Based on the rough surface contact model of fractal theory, combining the theory of van der Waals force and capillary force, the adhesion model of the rough surfaces was constructed, and the reliability of the model was verified through experiments. Research has found that after displacement between the two surfaces the main force of the adhesion force is capillary force. The capillary force decreases with the increasing of surface roughness, droplet volume, and contact angle. For an ultra-thin sapphire wafer with a diameter of 50.8 mm and a thickness of 0.17 mm, more than 1.4 N of normal adhesion force can be generated through the LSC method. Through the double-sides polishing experiment using the LSC method, an ultra-thin sapphire wafer with an average surface roughness (Ra) of 1.52 nm and a flatness (PV) of 0.968 μm was obtained.
Rotor fault diagnosis has attracted much attention due to its difficulties such as non-stationarity of fault signals, difficulty in fault feature extraction and low diagnostic accuracy of small samples. In order to extract fault feature information of rotors more effectively and to improve fault diagnosis precision, this paper proposed a fault diagnosis method based on variational mode decomposition (VMD) symmetrical polar image and fuzzy neural network. Firstly, the original rotor vibration signal is decomposed by using the VMD method and the relevant parameter selection algorithm of the VMD method is also proposed. Secondly, the intrinsic mode functions (IMF), which are sensitive to the signal characteristics, are selected for signal reconstruction based on a comprehensive evaluation factor method. As well, the reconstructed signal is transformed into a two-dimensional snowflake image through using the symmetrical polar coordinate method. Finally, the image features are extracted by the gray level co-occurrence matrix to form the state feature vector, which is input into the fuzzy neural network to realize the rotor fault diagnosis. Through the analysis of measured signals, the experimental results show that the proposed method can reach a higher recognition rate of 98% and the k-cross-validation experiment is used to demonstrate the robustness of the fuzzy neural network, and the average recognition accuracy of this experiment is 99.2%. Compared with some similar methods, the proposed method still has the highest fault recognition precision 98.4%, and the smallest standard deviation 0.5477.
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