Batch processing method for defect recognition of X-ray directional instrument

A defect recognition, batch processing technology, applied in character and pattern recognition, material analysis using radiation diffraction, instruments, etc., can solve problems such as system falling into local optimum and abnormal data powerlessness.

Active Publication Date: 2018-12-18
NORTHEASTERN UNIV
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Problems solved by technology

The FCM algorithm has the following two shortcomings: (1) When the initial clustering center of the FCM algorithm is poor, the sy...

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  • Batch processing method for defect recognition of X-ray directional instrument
  • Batch processing method for defect recognition of X-ray directional instrument
  • Batch processing method for defect recognition of X-ray directional instrument

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Embodiment Construction

[0076] Combined with the accompanying drawings, a technical scheme of a batch processing method for X-ray orientation instrument defect identification is described in detail, and the specific steps are as the overall process figure 1 Shown:

[0077] Step 1: Establish a sample library, including the data sample of the trochoidal curve of the standard single crystal crystal to be tested and each defect type, extract the feature vector of each trochoidal curve in the sample, and save the characteristics of each trochoidal curve of the sample vector, as a sample library;

[0078] This application takes sapphire as an example, and provides three sample knowledge bases of crystals corresponding to standard crystals, edge dislocations and screw dislocations. Figure 4 , Figure 5 , Figure 6 As shown, the calculated eigenvectors are shown in the table below, where the eigenvector variables are defined as Figure 7 Shown:

[0079] Table 1 Pattern eigenvectors

[0080]

[0081...

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Abstract

The invention provides a batch processing method for identifying defects of an X-ray directional instrument, belonging to the field of single crystal material processing. A method for detect defects in off-line batch single crystal is proposed, the eigenvector of the pendulum curve of single crystal is designed, so that the characteristics of the curve are abstracted, combined particle swarm optimization algorithm, and introduces the effective radius based on the density function, the traditional FCM algorithm is improved, the robustness of the lifting algorithm to the initialization of clustering centers is improved, falling into local optima is avoided, and interfering data is well filtered out, so as to realize clustering of batch data, only the clustering center eigenvector needs to bedetected. The defect types of the curves to be tested can be obtained according to the membership degree relation, wherein, the improved fuzzy transfer closure clustering algorithm proposed by the invention is included, the fuzzy similarity matrix is defined, the accuracy of the similarity calculation is ensured, and the invention provides a new idea and a realization mode for the crystal detection technology.

Description

technical field [0001] The invention belongs to the field of single crystal material processing, and in particular relates to a batch processing method for identifying defects of an X-ray orientation instrument. Background technique [0002] During the crystal growth process, the unavoidable problem is crystal defects, because of the inherent limitations of the technology, which will lead to the existence of non-ideal crystal structures. In industry, high-precision and high-efficiency X-ray diffraction technology is a commonly used non-destructive detection method for crystal defects. Single-crystal crystals diffract X-rays, and the corresponding characteristic curve is obtained by receiving the diffraction signal, that is, the trochoidal curve. Based on the trochoidal curve characteristics The quality characteristics of the crystal to be tested can be analyzed. [0003] At present, the automation level of the X-ray orientation instrument used for single crystal defect dete...

Claims

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Application Information

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IPC IPC(8): G06K9/62G01N23/20
CPCG01N23/20G06F18/23
Inventor 关守平王玉勇
Owner NORTHEASTERN UNIV
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