Preload Detection System Based on Ultrasonic Energy Transmission for Attached Joint Structures
Source abstract
Abstract
Abstract In the aerospace industry and nuclear power plants, bolt preload is a crucial metric for assessing the quality of installation and the state of service of the flange connection parts of pressure vessels. The primary technique for determining the preload force is the ultrasonic stress measurement method based on pulse time-of-flight. However, the current ultrasonic preload measuring approach is no longer applicable for unique bolted structures like pointed bolts and high acoustic attenuation bolts, as it is unable to receive the bolt end face echo. This work aims to address this issue by proposing a detection system based on interlayer ultrasonic energy transfer-based bolt preload measurement method. The ultrasonic energy transmission model of the sandwich’s rough interface is built using both classical contact theory and ultrasonic propagation theory. When paired with a finite element simulation, this model shows a linear relationship between the sandwich’s ultrasonic transmission signal energy and the bolt preload force. The relationship between the ultrasonic transmission energy and the bolt preload force under the ultrasonic frequency contact surface roughness and other factors was systematically investigated. A testing platform was set up to conduct the ultrasonic energy transmission test on the preload force of the bolt interlayer. According to the test results, there is a good linear relationship between the ultrasonic transmission signal energy and bolt preload obtained under various groups of parameters, R 2 is greater than 0.97, and the bolt preload measurement error range for flange bolts is between –5.4% and 5.6%. These findings suggest that the method presented in this paper has a promising future.
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Research context
OpenAlex topics: Ultrasonics and Acoustic Wave Propagation · Engineering Applied Research · Structural Health Monitoring Techniques
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
References (24)
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