Steel material performance prediction method based on EBSD and deep learning method
A steel material, deep learning technology, applied in instruments, design optimization/simulation, electrical digital data processing, etc., can solve problems such as complex organization and performance relationship, and achieve the effect of avoiding errors and high precision
Active Publication Date: 2021-06-25
NORTHEASTERN UNIV
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However, iron and steel materials involve long processes and complex manufacturing processes, and the relationship between structure and performance is more complicated. There are few related works under the iron and steel material system.
Although methods based on traditional physical metallurgical models can achieve performance prediction, these studies are based on manually extracted tissue information
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[0047] The specific implementation manners of the present invention will be further described in detail below in conjunction with the accompanying drawings and embodiments. The following examples are used to illustrate the present invention, but are not intended to limit the scope of the present invention.
[0048] A steel material performance prediction method based on EBSD and deep learning methods, such as figure 1 shown, including the following steps:
[0049] Step 1: Establish the original data set of the target steel material; establish the original data set of the target steel material through the EBSD experiment, the original data set contains X performance steel material image data, and the BC diagram included is not less than N groups;
[0050] Step 1.1: Carry out EBSD experiment to collect image data of target iron and steel material;
[0051] Conduct EBSD experiments in a random area of the target iron and steel material, the number of experimental groups is no...
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The invention provides a steel material performance prediction method based on EBSD and a deep learning method, and relates to the technical field of steel material performance prediction and deep learning application. According to the method, on the basis of the BC graph of the EBSD, the corresponding relation between the steel material structure (BC graph) and the performance is established by using a deep learning method convolutional neural network (CNN), and the performance prediction of the steel material is realized. On the basis of the BC graph of the EBSD, the corresponding relation between the steel material structure (BC graph) and the performance is established by using a deep learning method convolutional neural network (CNN), and the performance prediction of the steel material is realized.
Description
technical field [0001] The invention relates to the technical field of steel material performance prediction and deep learning application, in particular to a steel material performance prediction method based on EBSD and deep learning methods. Background technique [0002] The establishment of the relationship between microstructure and mechanical properties has always been the focus of attention in the field of steel materials. The establishment of the relationship between the microstructure and mechanical properties of traditional iron and steel materials mostly adopts the performance prediction method based on the physical metallurgical model. For example, to predict the yield strength of steel by superimposing empirical formulas of various strengthening mechanisms, it is generally necessary to extract microstructure information such as grain size in the microstructure by means of artificial abstraction or measurement. Therefore, the accuracy of conventional physical mo...
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IPC IPC(8): G06F30/27G06F119/02G06F119/14
CPCG06F30/27G06F2119/02G06F2119/14
Inventor 徐伟任达魏晓蓼沈春光黄健张朕王晨充
Owner NORTHEASTERN UNIV