Stress and defect detection of specimens based on tag array sensing technology
1
School of Chemical Engineering and Mechanical, Liaodong University, Dandong 118003, China
Submission date: 2024-07-03
Final revision date: 2024-08-26
Acceptance date: 2024-09-27
Online publication date: 2024-10-25
Publication date: 2024-10-25
Corresponding author
Diagnostyka 2024;25(4):2024405
KEYWORDS
TOPICS
ABSTRACT
To meet the quality and accuracy requirements of structural health detection, this study is based on the RFID tag array sensing technology as the core, and designs a method for metal specimen defect detection and material bending stress assessment. The experiment shows that the root mean square error of the designed fixed frequency analysis startup power algorithm and the error result of the R-squared index in the ultra-high frequency band are at the minimum level, which is suitable for the working frequency band of metal specimen defect detection. At the same time, the accuracy and recall index values of this algorithm are relatively high, located in the range of 84.41% -90.27% and 78.17% -90.26%, respectively. The application of tag array sensing defect detection technology in the evaluation of metal defect specimens and deflection bending stress is effective, and there are significant differences in the distribution of characteristic values and power levels between healthy and defective areas, indicating a good discrimination effect. This study enriches the theoretical foundation and application practice of tag array sensing technology in the field of structural non-destructive health monitoring, facilitates comprehensive stress monitoring of structures, and improves the robustness of structural monitoring schemes.
FUNDING
The research is supported by 2023 Dandong Guiding Science and Technology Plan (Liaodong University Joint Plan), "Research of Automotive Parts Inspection Platform Based on Computer Vision Technology", No. 10; 2024 Basic Scientific Research Project for Colleges and Universities of Liaoning Education Department, "Research on the Application of Computer Vision Technology in Mechanical Parts Inspection" [2024]No.136.
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