Precise measurement of bolt preload force is particularly important in the assembly process of aviation equipment, which is directly related to the normal working performance and indicators of the equipment. According to the acoustoelastic effect, a model considering theoretical error and measurement error is proposed. The relationship between the change of time-of-flight (CTOF) and the bolt preload is established by the model. With the increase of the preload, the CTOF approximately increases linearly. Therefore, a smart piezoelectric bolt was developed. Piezoceramic transducer is embedded in the bolt head at pre-determined spatial locations. Experimental results show that the relative error of the smart bolt is about 1%. In addition, the influence of contact pressure and contact phase is discussed. The results show that the characteristic of smart piezoelectric bolt eliminates the effect of the couplant and makes the measurement more precise than the measurement performed using the ultrasonic probe. This method provides a promising way to measure the bolt preload.

The traditional monitoring methods can only give warnings for the bolts with severe looseness. However, it is essential for the safety of bolted joints to detect the looseness of bolts at the very early stage. To this end, in this paper, coda wave interferometry (CWI)-based high-resolution bolt preload monitoring using a single piezoceramic transducer is proposed. According to the CWI and acoustoelastic theories, a theoretical model is established and the linear relationship between the time shifts of coda waves and the preload variations of the bolt is derived. An experiment, in which a piezoceramic transducer simultaneously functions as the actuator and sensor, was carried out to verify the effectiveness of the proposed method. Three lead zirconium titanate transducers at different locations of a bolted specimen are tested. The experimental results show that the time shifts of coda waves increase linearly with the decrease of bolt preload and the detectable resolution of bolt preload (DRBP) is up to 0.326%. The DRBP value proves that the proposed technique can successfully monitor bolt looseness at the very early stage. In addition, a comparison study is carried out between the CWI-based method and the energy-based wavelet packet decomposition (WPD) method, and the result shows that the preload sensitivity of the CWI-based method is about six times higher than that of the WPD approach. Therefore, the CWI-based method is an effective way for the in situ monitoring of bolt looseness, especially in the embryonic stage.

This paper presents a numerical simulation method to determine the surface morphology characteristics of metallic materials. First, a surface profiler (NV5000 5022s) was used to measure the surface, and the morphology data thereof were characterized. Second, fractal theory was used to simulate the surface profile for different fractal dimensions D and scale coefficients G, and statistical analyses of different surface morphologies were carried out. Finally, the fractal dimension D of the simulated morphology and the actual morphology were compared. The analysis showed that the error of fractal dimension D between the two morphologies was less than 10%; meanwhile, the comparison values of the characterization parameters of the simulated morphology and the actual morphology were approximately equal, and the errors were below 6%. Therefore, the current method used to evaluate the surface morphologies of parts processed by the grinding/milling method can be replaced by the simulated method using the corresponding parameters. This method makes it possible to theorize about the surface morphologies of machined parts, and provides a theoretical basis and reference value for the surface morphology design of materials, with the potential to improve the assembly quality of products.