No abstract

Bolted joints are widely used in aerospace and civil structures. Nevertheless, bolts may easily lose preload because of inappropriate initial preloads or time-varying servicing loads. Hence, bolt loosening monitoring is important for ensuring the safety and reliability of the bolt-jointed structures. In this paper, a guided wave method based on virtual time reversal (VTR) and tightness index representing refocusing capability is developed for bolt preload monitoring and to mitigate the requirement of baseline. In VTR, a response signal acquired from the bolted structure in fully tightened condition is reversed and used as the excitation signal for the bolted structure in loosening states. On this basis, the ratio of the energy of the refocused wave packet in final received wave signal to the energy of the entire signal is defined as tightness index (TI E ). In this way, the calculation of TI E does not need to compare with baseline acquired in fully tightened condition. The effects of roughness of contact surfaces on guided wave propagation and the refocusing capability are analyzed by a 2D FE model. The proposed method is also experimentally validated by bolt-jointed structures with single and multiple bolts. The experimental results show that TI E increased almost linearly with the decease of bolt preload and is effective at the early stage of bolt loosening. In addition, the effect of temperature on preload detection accuracy is weak. Furthermore, it is important to select a right frequency to ensure that the TI E is sensitive throughout the entire bolt torque range.

L-shaped bolt lap joints are commonly used in aerospace and civil structures. However, bolt joints are frequently subjected to loosening, and this has a significant effect on the safety and reliability of these structures. Therefore, bolt preload monitoring is very important, especially at the early stage of loosening. In this paper, a virtual time reversal guided wave method is presented to monitor preload of bolted L-shaped lap joints accurately and simply. In this method, a referenced reemitting signal (RRS) is extracted from the bolted structure in fully tightened condition. Then the RRS is utilized as the excitation signal for the bolted structure in loosening states, and the normalized peak amplitude of refocused wave packet is used as the tightness index (TIA). The proposed method is experimentally validated by L-shaped bolt joints with single and multiple bolts. Moreover, the selections of guided wave frequency and tightness index are also discussed. The results demonstrate that the relationship between TIA and bolt preload is linear. The detection sensitivity is improved significantly compared with time reversal (TR) method, particularly when bolt loosening is at its embryo stage. The results also show that TR method is an effective method for detection of the number of loosening bolts.

Capacitive micromachined ultrasonic transducers (CMUTs) are one of the appealing MEMS devices. Most studies treat CMUTs as rigid plates vibrating in open air, ignoring the mechanical boundary conditions for simplification and resulting in cumulative errors in coupled fields. This paper presents a new analytical model for the pull-in characteristics of the flat circular CMUT cell featuring sealed cavity. Utilizing the plate theory coupled with Boyle’s law, the paper establishes a strong relation between the pressures inside the sealed cavity and the pull-in characteristics for the first time. Not only did we point out that the existence of the pressure inside the sealed cavity cannot be omitted, but we also quantified the direct effect of the pressure ratios on the pull-in phenomenon. The pull-in voltages increase while the pull-in ratios decrease with the pressure ratios of the pressure inside the sealed cavity to the ambient pressure. The proposed calculation process delivers a good approximation of the pull-in voltages and displacements, which are consistent with COMSOL simulation results. Particularly, the percentage error of our calculation process is 6.986% for the worst case. Therefore, our proposed analytical model accurately and efficiently predicts the pull-in characteristics and this paper offers new perspectives and reference value in designing and modeling the CMUTs.

No abstract