公开(公告)号：US20240105370A1

公开(公告)日：2024-03-28

申请号：US18530485

申请日：2023-12-06

Applicant: NCS Testing Technology CO., LTD ,  University of Science and Technology Beijing

Inventor： Haizhou Wang ,  Runqiu Lang ,  Haiyang Chen ,  Yandong Wang ,  Lei Zhao ,  Changwang Zhu ,  Xiaofen Zhang ,  Lixia Yang ,  Dongling Li ,  Xuejing Shen ,  Yunhai Jia

IPC: H01F1/153 ,  C22C1/02 ,  C22C30/00 ,  C22C33/04 ,  C22C38/02 ,  C22C38/06 ,  C22C38/10

CPC classification number: H01F1/153 ,  C22C1/02 ,  C22C30/00 ,  C22C33/04 ,  C22C38/02 ,  C22C38/06 ,  C22C38/10 ,  C22C38/105 ,  C22C2202/02

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. The high-entropy soft magnetic alloy with 900 K high-temperature resistance of the present invention realizes the continuously diffuse distribution of nano-scale precipitates in the matrix structure by comprehensively regulating the microstructure configuration of the multi-principal element alloy, thus improving the soft magnetic properties of the alloy to a certain extent, and the processing route is simple and reliable, with high repeatability.

公开(公告)号：US20230205175A1

公开(公告)日：2023-06-29

申请号：US18116279

申请日：2023-03-01

Applicant: NCS Testing Technology CO.,LTD

Inventor： Lei Zhao ,  Haizhou Wang ,  Lixia Yang ,  Lei Yu ,  Xuebin Chen ,  Hui Wang ,  Xuejing Shen ,  Yunhai Jia ,  Dongling Li ,  Xing Yu

IPC: G05B19/4099

CPC classification number: G05B19/4099 ,  G05B2219/49023

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. The present invention integrates multiple functions, has high automatic control level, improves the experimental speed and experimental efficiency.

公开(公告)号：US11506650B2

公开(公告)日：2022-11-22

申请号：US17009117

申请日：2020-09-01

Applicant: The NCS Testing Technology Co., Ltd.

Inventor： Dongling Li ,  Weihao Wan ,  Jie Li ,  Haizhou Wang ,  Lei Zhao ,  Xuejing Shen ,  Yunhai Jia

IPC: G06T7/00 ,  G01N33/204 ,  G06T7/62 ,  G06T7/11 ,  G01N1/32 ,  G01N1/40

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. 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.