Theoretical and numerical investigation of stress mode shapes in multi-axial random fatigue

Experimental and numerical investigation on static and dynamic characteristics for curvilinearly stiffened plates using DST–BK model

Bolted connections have been widely applied in engineering structures, loosening will happen when bolted connections are subjected to continuous cyclic load, and a significant rotation between the nut and the bolt can be observed. Combining deep learning with machine vision, a bolt loosening detection method based on the fifth version of You Only Look Once (YOLOv5) is proposed, and the rotation of the nut is identified to detect the bolt loosening. Two different circular markers are added to the bolt and the nut separately, and then YOLOv5 is used to identify the circular markers, and the rotation angle of the nut against the bolt is calculated according to the center coordinate of each predicted box. A bolted connection structure is adopted to illustrate the effectiveness of the method. First, 200 images containing bolts and circular markers are collected to make the dataset, which is divided into a training set, verification set and test set. Second, YOLOv5 is used to train the model; the precision rate and recall rate are respectively 99.8% and 100%. Finally, the robustness of the proposed method in different shooting environments is verified by changing the shooting distance, shooting angle and light condition. When using this method to detect the bolt loosening angle, the minimum identifiable angle is 1°, and the maximum detection error is 5.91% when the camera is tilted 45°. The experimental results show that the proposed method can detect the loosening angle of the bolted connection with high accuracy; especially, the tiny angle of bolt loosening can be identified. Even under some difficult shooting conditions, the method still works. The early stage of bolt loosening can be detected by measuring the rotation angle of the nut against the bolt.

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Vision-Based Detection of Bolt Loosening Using YOLOv5

Dynamic shear fracture behaviors and “pseudo-plastic” constitutive model of carbon/carbon composite pins

Accurate stress responses are the basics for failure analysis of aerospace structures, but they still can be challenging in numerical simulations for dynamic systems. This work exploits the stress ...

Local Finite Element Refinement for Accurate Dynamic Stress via Modal Information Only

Frequency-dependent random fatigue of panel-type structures made of ceramic matrix composites

Finite element explicit dynamics simulation of motion and shedding of jujube fruits under forced vibration

Effective excitation conditions for the intense motion of the ginkgo seed-stem system during mechanical vibration harvesting

The multiscale model based on micro-mechanics failure theory is modified to consider complex internal structures, including a fiber random arrangement pattern and interface with the clustering method. Then, a feed-forward-neural-network (FFNN)-based damage evolution method is developed to evaluate the macroscale property degradation. The progressive damage analysis of open-hole laminates under compression is conducted to validate the modified multiscale method. The predicted results reveal that the interface results in the premature initiation of damage, and the fiber random arrangement pattern contributes to the decrease in the predicted compression responses. The developed FFNN-based method aimed at degradation results in an increase in the predicted compression strength. For the fiber random distribution pattern, the increase in percentage of predicted compressive strength is 6.0%, which is much larger than the value for the fiber diamond distribution pattern.

/pdf/modified-micro-mechanics-based-multiscale-model-for-damage-3bt5q40b.pdf

Modified Micro-Mechanics Based Multiscale Model for Damage Analysis of Open-Hole Composite Laminates under Compression

We proposed a gradient phononic crystal-Helmholtz cavity structure for simultaneous noise and vibration reduction. By simplifying the structure into a typical Helmholtz cavity and a phononic crystal, we derived the resonance frequency of this structure. Next, we use COMSOL software to calculate the transmission loss and transmissibility. Results of our analysis confirmed that the structure can successfully simultaneous reduce noise and vibration within 2500 Hz, and the range of the reduction frequency is close to 500 Hz. We also show different structures to realize the adjustability of the band gap and improve the practical of noise and vibration reduction. Graphical abstract This is the simulation diagram of phononic crystal-Helmholtz cavities. From the perspective of multiple physical fields, vibration reduction belongs to solid mechanics fields, while noise reduction belongs to pressure acoustics fields. In our paper, we both studied these two fields, the results show that this structure can simultaneous reduce noise and vibration within 2500 Hz, and the range of the reduction frequency is close to 500 Hz. In addition, the relationship between reduction frequency and the change of gradient is further studied. Results show that the frequency of the bandgap decreases with the number of unit cell increased. The center frequency of the bandgap decreases from 2000 to 1400 Hz, and it becomes stable at about 1400 Hz eventually.

Xiaochen Hang

Papers

Frequency-dependent random fatigue of panel-type structures made of ceramic matrix composites

Finite element explicit dynamics simulation of motion and shedding of jujube fruits under forced vibration

Effective excitation conditions for the intense motion of the ginkgo seed-stem system during mechanical vibration harvesting

Modified Micro-Mechanics Based Multiscale Model for Damage Analysis of Open-Hole Composite Laminates under Compression

A novel gradient phononic crystal-Helmholtz cavity structure for simultaneous noise and vibration reduction