This work aims to achieve the 6000t compression-shear test machine frame design with the lightweight. The force condition of the compression-shear test machine frame under limited working conditions is first analyzed, and the static analysis of the compression-shear test machine frame is performed using ABAQUS. Then, taking the volume of the frame of the compression-shear testing machine as the constraint condition, the topology optimization of the compression-shear testing machine frame is performed using the variable density method of topology optimization, and the model is reconstructed accordingly. Finally, not only the static characteristics of the frame before and after optimization but also the modal characteristics of the frame before and after optimization and the dynamic characteristics after sudden unloading are compared and analyzed. The results show that the weight of the frame decreases by 14.5% after optimization, and the maximum static stress of the frame is still less than the yield strength of the material; the maximum displacement is still less than the allowable maximum displacement, which meets the requirements of static strength and stiffness. The natural frequency of each mode is much greater than the working frequency, which meets the requirements of dynamic stiffness. After sudden unloading, the maximum dynamic stress of beams, columns, and base of the frame are less than the yield strength of materials, which meets the requirements of dynamic strength.
Loosening of bolts, which is a common form of failure in bolted connections, causes relative slippage between the connected surfaces. The bolts fail under the action of external shear forces due to fatigue and breakage, thereby affecting the service performance and connection strength
of the equipment, potentially resulting in major accidents. At present, condition monitoring, which is used to detect the tightness of bolt connections, has obtained acceptable results; however, most of them are still carried out under laboratory conditions and cannot be applied to engineering.
In addition, effective remedial measures should be implemented after detecting bolt looseness. On the basis of such problems, a multi-bolt looseness monitoring method based on machine vision and deep learning is proposed. At the same time, shape memory alloy is used in the design of a structure
that actively compensates for loose bolts. This method realises bolt recognition of the bolt connection structure through video monitoring and looseness monitoring of multi-target bolts at the same time. When the system detects that the bolts are loosened, an alarm signal is issued and, at
the same time, the control device is activated to compensate, to increase the time available for repair time and to ensure the service performance of major equipment.
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