The Ncs Testing Technology Co., Ltd.

Central Iron and Steel Research Institute

Weihao Wan

Haizhou Wang

Xuejing Shen

Lei Zhao

Dongling Li

Yuhua Lu

Yunhai Jia

Xing Yu

Xiaojia Li

Hui Wang

Chao Li

Qiang Li

Bing Han

Yongqing Wang

YiFei Zhu

Qingqing Zhou

Haozhou Feng

Qun Ren

Zhenzhen Wan

Feifei Feng

Jianqiu Luo

An apparatus and a method for preparing glow discharge sputtering samples for materials microscopic characterization are provided. The apparatus includes a glow discharge sputtering unit, a glow discharge power supply, a gas circuit automatic control unit, a spectrometer, and a computer. The structure of the glow discharge sputtering unit is optimized to be more suitable for sample preparation by simulation. By adding a magnetic field to the glow discharge plasma, uniform sample sputtering is realized within a large size range of the sample surface. The spectrometer monitors multi-element signal in a depth direction of the sample sputtering, so that precise preparation of different layer microstructures is realized. In conjunction with the acquisition of the sample position marks and the precise spatial coordinates (x, y, z) information, the correspondence between the surface space coordinates and the microstructure of the sample is conveniently realized.

Apparatus and method for preparing glow discharge sputtering samples for material microscopic characterization

The present application relates to a method for statistical distribution characterization of dendritic structures in original position of single crystal superalloy, and relates to the technical field of analysis of metal material composition and microstructure, comprising the following steps: step 1, processing a to-be-tested sample and determining a calibration coefficient; step 2, obtaining a two-dimensional element content distribution map of the to-be-tested sample; and step 3, determining the number and average spacing of primary dendrites. A composition distribution region analyzed in the present application is larger than the area of a distribution region of the traditional microscopic analysis method, and the sample preparation is simple. The distribution, number and average spacing of the primary dendrites can be obtained without metallographic corrosion sampling. Therefore, the present invention has the advantages of large statistical field of view, high efficiency and complete information, and the statistical data is more accurate and reliable.

Method for statistical distribution characterization of dendritic structures in original position of single crystal superalloy

The invention belongs to the technical field of quantitative statistical distribution analysis for micro-structures of metal materials, and relates to a method for automatic quantitative statistical distribution characterization of dendrite structures in a full view field of metal materials. According to the method based on deep learning in the present invention, dendrite structure feature maps are marked and trained to obtain a corresponding object detection model, so as to carry out automatic identification and marking of dendrite structure centers in a full view field; and in combination with an image processing method, feature parameters in the full view field such as morphology, position, number and spacing of all dendrite structures within a large range are obtained quickly, thereby achieving quantitative statistical distribution characterization of dendrite structures in the metal material. The method is accurate, automatic and efficient, involves a large amount of quantitative statistical distribution information, and is statistically more representative as compared with the traditional measurement of feature sizes of dendrite structures in a single view field.

Method for automatic quantitative statistical distribution characterization of dendrite structures in a full view field of metal materials

The invention belongs to the technical field of the quantitative statistical distribution analysis of the features from characteristic images of microstructures and precipitated phases in metal materials, and relates to a quantitative statistical distribution characterization method of precipitate particles with the full field of view in a metal material. The method comprises the following steps of electrolytic corrosion of a metallic material specimen, automatic collection of characteristic images of microstructure, automatic stitching and fusion of the full-view-field microstructure images, automatic identification and segmentation of the precipitate particles and quantitative distribution characterization of the precipitate particles with the full field of view in a large-range scale. By establishing a mathematic model, the large-range automatic stitching and fusion of the characteristic images of the full-view-field microstructures in a characteristic region and the automatic segmentation and identification of the precipitate particles are realized; and the quantitative statistical distribution characterization information of the full-view-field morphology, quantity, size, distribution and the like of plentiful precipitated phases in a larger range is quickly obtained. The method has the features of being accurate, high-efficiency and informative in quantitative distribution characterization, as well as has much more statistical representativeness compared with conventional single-view-field quantitative image analysis.

Full-view-field quantitative statistical distribution characterization method of precipitate particles in metal material

An apparatus and method for a large-scale high-throughput quantitative characterization and three-dimensional reconstruction of a material structure. The apparatus having a glow discharge sputtering unit, a sample transfer device, a scanning electron microscope unit and a GPU computer workstation. The glow discharge sputtering unit can achieve large size (cm order), nearly flat and fast sample preparation, and controllable achieve layer-by-layer ablation preparation along the depth direction of the sample surface; rapid scanning electron microscopy (SEM) can achieve large-scale and high-throughput acquisition of sample characteristic maps. The sample transfer device is responsible for transferring the sample between the glow discharge sputtering source and the scanning electron microscope in an accurately positioning manner. The GPU computer workstation performs splicing, processing, recognition and quantitative distribution characterization on the acquired sample characteristic maps, and carries out three-dimensional reconstruction of the structure of the sample prepared by layer-by-layer sputtering.

Apparatus and method for large-scale high throughput quantitative characterization and three-dimensional reconstruction of material structure

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Beijing, China

Weihao Wan