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.

The nonlinear characteristics of bolted connections are of significant importance for analyzing the mechanical performance of structures. The Iwan model is well-known and has been widely applied; its limitation is that it is not convenient for complex structures with multiple bolted connections. To simplify the modeling process, a material with the force-displacement characteristics of the Iwan model is proposed and applied to the bolted connection region, which can convert the nonlinearity of the bolted connection into the nonlinearity of the material. The constitutive relation of the proposed Iwan-based material is determined by the force-displacement equation of the bolted connection under load and the elastic-plastic hypothesis. The proposed Iwan-based material is implemented using the UMAT subroutine of ABAQUS, and the properties of the Iwan-based material are assigned to a solid finite element for an equivalent modeling of bolted connections. Through comparisons with the s imul ation results of the AIBE, the feasibility of the equivalent modeling method for the force-displacement relationship of the original Iwan model is verified, and through comparisons with the simulation results and experimental results of a detailed 3D FE model of the bolted connection, the universality of the equivalent modeling method is verified. The results show that the equivalent modeling method can well restore the statics characteristics of bolted structures under cyclic loading and can be applied to complex combined structures. The method is more convenient for establishing the finite element model of bolted connections and has more flexibility in adjusting parameters than traditional methods.

In order to improve the dynamic balancing accuracy of the micromotor armature, a method of V-shaped milling based on a discrete vector model for unbalance correction is proposed. The discrete vector model is fitted according to the parameters of the milling cutter and rotor, and then all the unit unbalance vectors in the discrete vector model are added to the milling center. The numerical relationship between the milling depth and the removal of the mass unbalance vector is obtained, and the accuracy of the model is verified via comparison with the data of the simulation experiments. The complexity of the integral formula of the numerical milling model makes it difficult to apply in practice. The discrete vector model does not require integration of the numerical formula and only considers the milling area as being composed of countless discrete blocks, which greatly simplifies the process of solving the unbalance vector. In view of the different thicknesses of the tooth surface of the armature, in order to avoid damage to the armature during milling, the unbalanced vector is decomposed at the center of the tooth surface by force decomposition. The experimental results show that this proposed method can effectively improve the dynamic balancing accuracy of the micromotor armature.