• Structural Monitoring and Maintenance
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• 제4권2호
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• pp.153-173
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• 2017

An improved method is presented to estimate the axial force of a bar member with vibrational measurements based on modified Timoshenko beam theory. Bending stiffness effects, rotational inertia, shear deformation, rotational inertia caused by shear deformation are all taken into account. Axial forces are estimated with certain natural frequency and corresponding mode shape, which are acquired from dynamic tests with five accelerometers. In the paper, modified Timoshenko beam theory is first presented with the inclusion of axial force and rotational inertia effects. Consistent mass and stiffness matrices for the modified Timoshenko beam theory are derived and then used in finite element simulations to investigate force identification accuracy under different boundary conditions and the influence of critical axial force ratio. The deformation coefficient which accounts for rotational inertia effects of the shearing deformation is discussed, and the relationship between the changing wave speed and the frequency is comprehensively examined to improve accuracy of the deformation coefficient. Finally, dynamic tests are conducted in our laboratory to identify progressive axial forces of a steel plate and a truss structure respectively. And the axial forces identified by the proposed method are in good agreement with the forces measured by FBG sensors and strain gauges. A significant advantage of this axial force identification method is that no assumption on boundary conditions is needed and excellent force identification accuracy can be achieved.

• 한국소음진동공학회논문집
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• 제19권4호
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• pp.386-392
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• 2009

This paper reports on the research which investigates acoustic signals acquired in progressive compressing, hole blanking, shearing and burr compacting process. The work piece is the head pin of the electric connector, whose raw material is the preformed steel bar. An acoustic sensor was set on the bed of hydraulic press. Because the acquired signals include the dynamic characteristics generated for all the processes, it is required to investigate signal characteristics corresponding to unit process. The corresponding dynamic characteristics to the respective process were first studied by analyzing the signals respectively acquired from compressing, blanking and compacting process. The combined signals were then periodically analyzed from the grinding to the grinding in the sound frequency domain and in the ultrasonic wave. The frequency of around 9 kHz in the sound frequency domain was much correlated to the tool wear. The characteristic frequency in the acoustic emission domain between 100 kHz and 500 kHz was not only clearly observed right after tool grinding but its amplitude was also related to the wear. The frequency amplitudes of 160 kHz and 320 kHz were big enough to be classified by the noise. The noise amplitudes are getting bigger, and their energy was much bigger as coming to the next regrinding. The signal analysis was based on the real time data and its frequency spectrum by Fourier Transform. As a result, the acousto-ultrasonic signals were much related to the tool wear progression.