Design And Development Of Detecting System For X-ray Rocking Curve Instrument

With the rapid development of electronic information industry, the quality and the processing technology of the single crystals receive more and more attention, in which accurate detection of the quality of the single crystals is a key process. The detection technology of the single crystals by X-ray diffraction is a nondestructive testing, which is a commonly used technical means in industry. We can get the rocking curve of the single crystals by the X-ray diffraction. Based on analysis of the rocking curve, you can get some quality characteristic parameters of the single crystals detected. Therefore, the design and development of X-ray rocking curve instrument used for the quality detection of the single crystals has important practical significance.For the requirements of the X-ray rocking curve orientation equipment, the paper uses the CPU313C-2DP of Siemens PLC control module as the hardware control core and Visual C++ development environment as the software platform for both hardware and software design and development to achieve the X-ray rocking curve detecting system functions. The paper has designed and developed the PLC lower machine and the PC upper computer. The PLC lower machine’s control section includes:stepper motor motion control, analog signal and encoder signal acquisition and serial port data communication. The PC upper computer application program includes:real-time display of data acquisition, secondary data processing and data storage and conversion. PLC lower machine and PC host computer complete the function of the X-ray diffraction signal’s acquisition and analysis and meet the technical requirements of the work site.After completed the system hardware and software design, the paper has carried on the theoretical study of the detection method based on the rocking curve of crystal defect. On the basis of the characteristic parameters extraction, the paper carried out the clustering analysis and then establish the class identification model based on BP neural network. After, combined with the fuzzy matching algorithm, the paper completed the precise identification of the defect model. The simulation results prove the validity of the pattern recognition model.