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A new method for detecting bolt preload based on a combination of longitudinal and transverse ultrasonic waves

Sep 2026 · Smart materials and structures (Print) · Vol 35 · 0 citations · 33 references
Physics

Abstract

Accurate measurement of bolt preload is critical for ensuring the performance and operational safety of mechanical structural connections. However, traditional modal-conversion-based longitudinal and transverse wave (T-wave) testing methods suffer from low T-wave excitation efficiency, insufficient signal stability, and limited feature-extraction accuracy. Taking an M36 × 3–350 mm bolt as the research subject, this study proposes an ultrasonic testing method based on direct excitation of T-waves using the D15 shear mode of a piezoelectric transducer. It simultaneously incorporates the tT/ tL time ratio feature to characterize the preload and employs a cubic spline interpolation algorithm to reconstruct the discretely sampled signals, thereby improving peak localization accuracy and enhancing the precision of time-of-flight extraction. The research results indicate: (1) Compared to traditional modal conversion excitation methods, direct excitation via the D15 shear mode significantly improves T-wave excitation efficiency and modal purity. (2) The sound time ratio between transverse and longitudinal waves exhibits a significant linear negative correlation with bolt pretension and can serve as an effective feature parameter for characterizing changes in bolt pretension; numerical simulations agree well with experimental results, with the overall measurement error controlled within 3%. (3) The use of a cubic spline interpolation algorithm enables the raw sampling interval to be increased from 0.01–0.0001 μs, improving the positioning accuracy of the first-arrival time of ultrasonic waves and enabling the effective identification of minute changes in preload. These research findings provide a theoretical foundation and technical support for bolt connection condition monitoring and intelligent structural health monitoring under complex operating conditions.

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