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基于无损检测的人工神经网络在钢种分类中的应用

Artificial Neural Networks in Classification of Steel Grades Based on Non-Destructive Tests.

作者信息

Beskopylny Alexey, Lyapin Alexandr, Anysz Hubert, Meskhi Besarion, Veremeenko Andrey, Mozgovoy Andrey

机构信息

Department of Transport Systems, Faculty of Roads and Transport Systems, Don State Technical University, Gagarin, 1, 344000 Rostov-on-Don, Russia.

Department of Information Systems in Construction, Faculty of IT-systems and Technologies; Don State Technical University, Gagarin, 1, 344000 Rostov-on-Don, Russia.

出版信息

Materials (Basel). 2020 May 27;13(11):2445. doi: 10.3390/ma13112445.

DOI:10.3390/ma13112445
PMID:32471095
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC7321333/
Abstract

Assessment of the mechanical properties of structural steels characterizing their strength and deformation parameters is an essential problem in the monitoring of structures that have been in operation for quite a long time. The properties of steel can change under the influence of loads, deformations, or temperatures. There is a problem of express determination of the steel grade used in structures-often met in the practice of civil engineering or machinery manufacturing. The article proposes the use of artificial neural networks for the classification and clustering of steel according to strength characteristics. The experimental studies of the mechanical characteristics of various steel grades were carried out, and a special device was developed for conducting tests by shock indentation of a conical indenter. A technique based on a neural network was built. The developed algorithm allows with average accuracy-over 95%-to attribute the results to the corresponding steel grade.

摘要

评估表征结构钢强度和变形参数的力学性能是长期运行结构监测中的一个重要问题。钢材性能会在载荷、变形或温度的影响下发生变化。在土木工程或机械制造实践中经常遇到结构中所用钢种的快速确定问题。本文提出使用人工神经网络根据强度特性对钢材进行分类和聚类。对各种钢种的力学特性进行了实验研究，并开发了一种用于通过圆锥压头冲击压痕进行测试的特殊装置。构建了一种基于神经网络的技术。所开发的算法能够以超过95%的平均准确率将结果归属于相应的钢种。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/44e7/7321333/957c8218f9f2/materials-13-02445-g012.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/44e7/7321333/ead2ee611b3e/materials-13-02445-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/44e7/7321333/3a39e4330c02/materials-13-02445-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/44e7/7321333/cefa8c6b482f/materials-13-02445-g008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/44e7/7321333/a7a62b8b2eea/materials-13-02445-g009.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/44e7/7321333/8892237a0672/materials-13-02445-g010.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/44e7/7321333/4b22e743333f/materials-13-02445-g011.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/44e7/7321333/957c8218f9f2/materials-13-02445-g012.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/44e7/7321333/b1c7b08e4f9d/materials-13-02445-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/44e7/7321333/30bffc0a3d22/materials-13-02445-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/44e7/7321333/ae9fd1bede25/materials-13-02445-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/44e7/7321333/02779bc84ad8/materials-13-02445-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/44e7/7321333/7f29f62107df/materials-13-02445-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/44e7/7321333/ead2ee611b3e/materials-13-02445-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/44e7/7321333/3a39e4330c02/materials-13-02445-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/44e7/7321333/cefa8c6b482f/materials-13-02445-g008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/44e7/7321333/a7a62b8b2eea/materials-13-02445-g009.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/44e7/7321333/8892237a0672/materials-13-02445-g010.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/44e7/7321333/4b22e743333f/materials-13-02445-g011.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/44e7/7321333/957c8218f9f2/materials-13-02445-g012.jpg

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