An Approach of Single-Crystal Defect Detection Using X-Ray Orientation Instrument

An intelligent and rapid detection approach based on an X-ray orientation instrument is proposed to identify the single-crystal defects through online single and offline batch defection. This method can effectively solve the problems of current detection equipment, such as longer detection time, higher cost, and human involvement. A total of eight factors in two pattern vectors that can express the rocking curve characteristics of the crystal structure are selected as the feature factors for defect detection. An improved algorithm of the fuzzy transitive closure clustering (FTCC) is presented to identify the defect type of one single-crystal online based on the real-time data, satisfying the real-time requirement of online defect detection. An improved algorithm of the fuzzy c-means that can effectively solve the problems of the bad initial clustering centers and abnormal data, combined with the improved algorithm of the FTCC, is employed to identify the defect types of batch single-crystals offline based on many measured data and to meet the rapid requirement of batch single-crystal defect identification. The sapphire is selected as an example of the single crystal to identify the defects both online and offline, and the experiment results indicate that the proposed method is a rapid and effective means of the defect detection that meets industrial manufacturing requirements.