It is of great significance to study the dynamic characteristics of twin ball screw (TBS) feed system to improve the precision of gantry-type dual-driven computer numerical control (CNC) machine tools. In this paper, an equivalent dynamic model of the TBS feed system is established utilizing lumped mass method considering the stiffness of joints. Equivalent axial stiffness of screw-nut joints and bearing joints are both calculated by Hertz contact theory. Furthermore, a friction model is proposed because the friction force of the screw nut affects the stiffness of the joints. Then, the friction parameters are obtained by using the nonlinear system identification method. Meanwhile, a finite element model (FEM) is developed to assess the dynamic characteristics of TBS feed system under the stiffness of joints. Finally, validation experiments are conducted, and the results show that the positions of the nut and the velocities of worktable greatly affect the dynamic characteristics of the TBS feed system. Compared with the theoretical calculation, FEM and experiments indicate that the dynamic modeling proposed in this article can reach a higher accuracy.

Security and reliability are important issues that must be paid attention to during the operation of rotating machinery. If defects can be found in the early stage, there will be enough time to take maintenance measures and realize the stable operation of equipment. However, the presence of noise, shaft rotation signals, gear meshing signals, and other interfering factors often obfuscate fault signals, rendering the early detection of defects an arduous undertaking. Against this backdrop, this study presents an advanced approach for early defect detection, integrating the virtues of variational mode decomposition (VMD) and multiscale singular value decomposition (MSVD). Initially, a novel evaluation index is constructed by combining envelope entropy and envelope spectrum sparsity. Based on this a method is proposed to adaptively determine the critical parameters of VMD, enabling the adaptive decomposition of vibration signals into a series of modal components. The optimal sensitive components are then discerned utilizing the characteristic frequency intensity coefficient index. Subsequently, to address the limitations of single VMD methods in effectively suppressing low-frequency noise, the MSVD method is proposed for effective noise reduction, which reconstructs the signal after SVD of the signal within each segment through the operation of successive signal segmentation. Ultimately, envelope spectrum analysis is conducted on the reconstructed signal, facilitating the precise extraction of fault characteristic frequency information and enabling early fault identification. The efficacy of this novel methodology is evaluated through simulations and actual vibration signals, successfully discerning early faults afflicting rotating machinery.

The performance of a gearbox is sensitive to failures, especially in the long-term high speed and heavy load field. However, the multi-fault diagnosis in gearboxes is a challenging problem because of the complex and non-stationary measured signal. To obtain fault information more fully and improve the accuracy of gearbox fault diagnosis, this paper proposes a feature extraction method, hierarchical refined composite multiscale fluctuation dispersion entropy (HRCMFDE) to extract the fault features of rolling bearing and the gear vibration signals at different layers and scales. On this basis, a novel fault diagnosis scheme for the gearbox based on HRCMFDE, ReliefF and grey wolf optimizer regularized extreme learning machine is proposed. Firstly, HRCMFDE is employed to extract the original features, the multi-frequency time information can be evaluated simultaneously, and the fault feature information can be extracted more fully. After that, ReliefF is used to screen the sensitive features from the high-dimensional fault features. Finally, the sensitive features are inputted into the optimized regularized extreme learning machine to identify the fault states of the gearbox. Through three different types of gearbox experiments, the experimental results confirm that the proposed method has better diagnostic performance and generalization, which can effectively and accurately identify the different fault categories of the gearbox and outperforms other contrastive methods.

With the development of welding automation and intelligence, computer vision-based welding process monitoring technologies have become increasingly applied. However, interference factors such as welding fumes, spatter, and intense arc light in the welding environment significantly affect the performance of existing welding monitoring and control technologies. To address this issue, this paper proposes a novel method for extracting molten pool dimensions that integrates rule-based and data-driven vision approaches and introduces a welding speed control system that considers the stability of the welding process. The goal of this study is to develop a method that not only monitors the molten pool’s size but also evaluates its stability. In this system, the first stage improves the accuracy of molten pool recognition and dimension extraction through an enhanced method, while the second stage introduces a speed adjustment factor based on welding stability to achieve stable control of welding speed. Experimental results demonstrate that the proposed method shows high adaptability and extraction accuracy under various interference conditions. The welding speed control strategy significantly enhances the stability and symmetry of the welding process, with performance improvements of approximately 45% in stability compared to the conventional method. This leads to an overall improvement in weld quality.

The fault diagnosis of rotating machinery is vital in industry but traditionally depends on manual expertise, requiring substantial resources. To improve diagnostic accuracy, enable effective condition monitoring, and minimize the impact of faults on operations, advanced diagnostic techniques are essential. Hence, we propose an advanced fault diagnosis framework that leverages improved particle swarm optimization (IPSO), variational mode decomposition (VMD), and probabilistic neural networks (PNN) to accurately diagnose faults in rotating machinery using gear and rolling bearing vibration signals. Initially, the vibration signals are decomposed into intrinsic mode functions via VMD, enabling the capture of subtle but critical fault features. To address parameter selection challenges in VMD, we employed IPSO to optimize the VMD parameters, ensuring the optimal decomposition effect. Further, we refined the feature set by applying Laplace fraction optimization and feature dimensionality reduction, isolating sensitive features that serve as input to a PNN-based fault classification model. Experimental results demonstrated that this IPSO-VMD-PNN framework achieves high diagnostic accuracy for various fault types, establishing it as an effective tool for fault identification in rotating machinery.

Industrial robots are widely used in welding operations because of their high production efficiency. The structure of the robot and the complex stress conditions during welding operations lead to the vibration of the end of robot, which leads to welding defects. However, current vibration suppression techniques for welding robots usually only consider the robotic performance while overlooking their impact on the welding metal forming process. Therefore, based on the influence of robot vibration on welding pool stability during the welding process, a new welding robot vibration suppression method is proposed in this paper, along with the establishment of a welding pool stability assessment model. The proposed vibration suppression algorithm is based on the optimization of the welding trajectory. To enhance the performance of the method, the Particle Swarm Optimization (PSO) algorithm is applied to optimize the joint angular velocity and angular acceleration. Finally, robot welding experiments are designed and conducted. By comparing vibration measurement data and welding quality before and after the vibration suppression, the effectiveness and stability of the proposed method are validated.