The Ncs Testing Technology Co., Ltd.

Central Iron and Steel Research Institute

The Boeing Company

Central Iron & Steel Research Institute Co., Ltd

Xuejing Shen

Haizhou Wang

Lei Zhao

Dongling Li

Yunhai Jia

Weihao Wan

Hui Wang

Xing Yu

Jia Liu

Yuhua Lu

Jun Yao

Han Wu

Xiaojia Li

Ge Zhao

Zongxin Liu

Yanbin Hu

Jiaqing Zeng

Libin Yang

Xiaoxia Shi

Chao Li

Yang Wang

Peng Wang

Qiang Li

Peng Xu

Rui Shen

Lixia Yang

Ying Lin

Ying Zhao

Bing Han

Ling Liu

Hongwei Shi

Guang Feng

YiFei Zhu

Yongqing Wang

Lizhi Ren

Xiaopeng Li

Zhongnan Bi

Mingbo Liu

Feifei Guo

Feipeng Cui

Suran Liu

Zhenzhen Wan

Qun Ren

Haozhou Feng

Qingqing Zhou

Ya Peng

Hailong Qin

Wenyi Cai

Changwang Zhu

Danqi Huang

Feifei Feng

Jianqiu Luo

An adaptive characteristic spectral line screening method and system based on atomic emission spectrum are provided, the method includes: using a set characteristic screening optimization method to perform a plurality of optimization rounds of characteristic screening, obtaining an initialized spectral dataset of each round of the characteristic screening and initialized characteristic population genes; obtaining an optimal characteristic population gene of each round by a set analysis method, a fitness function, and an iteration of a genetic algorithm; obtaining an optimized characteristic spectral information set when the plurality of optimization rounds reach set optimization rounds; performing combination statistics and discriminant analyses on the optimized characteristic spectral information set to complete an adaptive characteristic spectral line screening. The disclosure can efficiently and automatically screen out the characteristic spectral lines that meet the analysis requirements in the complex atomic emission spectrum, thus ensuring the effectiveness and accuracy of screening the characteristic spectral lines.

Adaptive characteristic spectral line screening method and system based on atomic emission spectrum

A method for quantitatively characterizing a dendrite segregation and dendrite spacing of a high-temperature alloy ingot is disclosed. The method includes preparation and surface treatment of the high-temperature alloy ingot, selection of calibration sample and determination of an element content, establishment of quantitative method for elements in micro-beam X-ray fluorescence spectrometer, quantitative distribution analysis of element components of the high-temperature alloy, quantitative characterization of characteristic element line distribution of high-temperature alloy, and analysis of a characteristic element line distribution map and statistics of a secondary dendrite spacing.

Method for quantitatively characterizing dendrite segregation and dendrite spacing of high-temperature alloy ingot

A component residual stress testing platform based on neutron diffraction and experimental method thereof are provided, the testing platform includes a component support, a rotating mainshaft, a first thrust cylindrical roller bearing, a first cylindrical roller bearing, a bearing spacing sleeve, a second cylindrical roller bearing, a sleeve, and a first fixed baffle. The rotating mainshaft is disposed on the component support. The first thrust cylindrical roller bearing, the first cylindrical roller bearing, the bearing spacing sleeve and the second cylindrical roller bearing are sleeved on the rotating mainshaft, the sleeve is sleeved outside the first cylindrical roller bearing, the bearing spacing sleeve and the second cylindrical roller bearing, a component to be tested is sleeved on the sleeve. The testing platform can support, move, tilt and rotate the component to be tested in a process of a residual stress testing.

Component residual stress testing platform based on neutron diffraction and experimental method thereof

A high-throughput 3D printing system for preparing multi-component, small sized samples, includes a raw material supply module, providing a various kinds of metal powders for printing small sized samples; the first mixer module, mixing the metal powders obtained from the raw material supply module to generate the first blended metal powders; the second mixer module, mixing the first mixed metal powders, in order to generate the second blended metal powders for printing small sized samples; the first printing module, printing the secondary blended metal powder into a small-sized sample; a control module, controlling other functional modules of the high-throughput 3D printing system for generating small-size samples.

High-throughput 3D printing system for preparing multi-component, small sized samples

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

A fully automatic online aero aluminum sorting and recovery system based on LIBS (Laser Induced Breakdown Spectroscopy) technology, which belongs to the field of aero aluminum sorting and recovery technology, and is suitable for online sorting, detection and recovery of large batch of aero aluminum. The fully automatic online aero aluminum sorting system based on LIBS technology provided in the present invention consists of six portions: a sample feeding unit (), a surface treatment unit (), a material positioning unit (), a LIBS analysis and detection unit (), a transfer unit () and a sorting and recovery unit (). The system according to the invention can be used to realize the automatic online detection, sorting and recovery of aero aluminum, and the system does not have requirements on the surface condition of the recovered aero aluminum samples. The sorting accuracy rate is greater than 90% and the sorting rate is not less than 1 block per second.

Automated aero aluminum scrap sorting system based on laser induced breakdown (LIBS) technique

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

The invention relates to a full-field statistical & characterizing method of fluid micro-explored strain for alloy microstructure, comprising the following steps: a. grinding and polishing the surface of an alloy sample to mirror with no grinding defects, and then determining a to-be-measured area on the surface of the alloy sample; b. utilizing a white light interferometry 3D surface profiler to perform initial morphology measurement on the surface of an alloy sample; c. utilizing an isostatic pressing technology to obtain the microstructure deformation on the surface of the alloy sample, and then utilizing a white light interferometry 3D surface profiler to perform deformed morphology measurement on the surface of the alloy sample to obtain a changing spectrum of micro morphology of the microstructures on the surface of the alloy; and d. performing trans-scale and rapid quantitative statistical distribution characterization on the morphology change before and after isostatic pressing of the microstructures in the to-be-measured area of the alloy, so as to obtain a corresponding full-field metallography. In the present invention, the sample pretreatment is simple, the analysis speed is rapid, the scanning area is large, and the requirement of high throughput trans-scale analysis can be satisfied, so as to instruct the extraction of the material metallography feature unit.

Full-field statistical and characterizing method of fluid micro-explored strain for alloy microstructure

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Beijing, China

Xuejing Shen