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 relates to a test system and method capable of simultaneously carrying out a high-throughput test of mechanical properties for miniature specimens. The system comprises one workstation (17) and a plurality of specimen test modules (16) installed horizontally or vertically on a workbench (15), wherein the workstation (17) comprises an operation interface, a data processing unit and a load output unit; each specimen test module (16) comprises a drive unit (5), an interchangeable clamp unit (8), a displacement sensor (2), and a load sensor (14); the workstation (17) controls the drive unit (5) of the specimen test module (16) and receives detection data of the displacement sensor (2) and the load sensor (14); each specimen test module (16) optionally performs mechanical property testing independently; and the workstation (17) controls simultaneously started testing of a plurality of specimens (9).

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.