Journal
MATERIALS
Volume 13, Issue 11, Pages -Publisher
MDPI
DOI: 10.3390/ma13112445
Keywords
non-destructive test; machine learning; clustering; steel; cone indentation; impact; artificial neural networks
Categories
Funding
- Russian Foundation for Base Research [18-01-00715-a]
Ask authors/readers for more resources
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.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available