Abstract: The present invention discloses a high-entropy soft magnetic alloy with 900 K high-temperature resistance, comprising Fe, Co, Ni, Si and Al, and the atomic percent of the alloy composition is expressed as FexCoyNizSimAln, wherein x=40%-80%, y=20%-60%, z=0-30%, m=0-20%, n=0-20%, and x+y+z+m+n=100%; the atomic percent of other doping elements is p=0-5%, and 0.5?m/n?3; the performance indexes of the material include: at room temperature, saturation magnetization Ms=90-150 emu/g, and coercive force Hc=0.1-15 Oe; and at 900 K, saturation magnetization Ms=70-130 emu/g, and coercive force Hc=0.1-25 Oe.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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 (1), a surface treatment unit (2), a material positioning unit (3), a LIBS analysis and detection unit (4), a transfer unit (5) and a sorting and recovery unit (6). 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.

Abstract: Aspects disclosed herein include an electrochemical cell comprising: an anode comprising: a first surface comprising aluminum metal or an aluminum alloy; a liquid metal on the first surface, the liquid metal being in liquid state during operation of the battery and the liquid metal having a different composition than that of the first surface; and aluminum-rich dendrites extending from the first surface and in contact with an electrolyte; a positive electrode; and the electrolyte between the positive electrode and the negative electrode, the electrolyte being capable of conducting ions.

Abstract: The present invention discloses an integrated research and development system for high-throughput preparation and statistical mapping characterization of materials, comprising: a high-throughput preparation module, a high-throughput characterization module, an automatic control module and a statistical mapping data processing module; the high-throughput preparation module is used for preparing a multi-component combinatorial-sample; the high-throughput characterization module comprises a plurality of different high-throughput characterization devices; the automatic control module comprises a special sample box, a sample moving platform, an intelligent mechanical arm and a synchronous control system; and the statistical mapping data processing module is used for constructing a statistical mapping constitutive model corresponding to position mapping according to the composition, microstructure and performance data of the combinatorial-sample.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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 (1), a surface treatment unit (2), a material positioning unit (3), a LIBS analysis and detection unit (4), a transfer unit (5) and a sorting and recovery unit (6). 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.

Abstract: 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 (1), a surface treatment unit (2), a material positioning unit (3), a LIBS analysis and detection unit (4), a transfer unit (5) and a sorting and recovery unit (6). 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.

Abstract: The present invention discloses a high throughput statistical characterization method of metal micromechanical properties, which comprises: grinding and polishing a metal sample until specular reflection finish satisfies a test requirement; marking position coordinates of a to-be-measured area on the metal sample by a microhardness tester to ensure the comparison of the same to-be-measured area; conducting an isostatic pressing strain test on the to-be-measured area by an isostatic pressing technology; and comparing high throughput characterization of components, microstructures, microdefects and three-dimensional surface morphology of the metal sample before and after isostatic pressing strain to obtain the full-view-field cross-scale high throughput statistical characterization of micromechanical property uniformity of the metal sample.

Abstract: 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.

Abstract: The present invention discloses, a full-view-field quantitative statistical distribution representation method for microstructures of ?? phases in a metal material, comprising the following steps: step a: labeling ?? phases, cloud clutters and ? matrixes by Labelme, and then making standard feature training samples; step b: building a deep learning-based feature recognition and extraction model by means of BDU-Net; step e: collecting ?? feature maps in the metal material to be detected; step d: automatically recognizing and extracting the ?? phases; and step e: performing in-situ quantitative statistical distribution representation on the ? phases in the full view field within a large range.

Abstract: 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.

Abstract: 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.

Abstract: 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.