Linsen Song

Localized stress concentration and defect accumulation are prone to occurring during metal spinning because of the coupled effects of complex loading and interfacial friction. In this study, a molecular dynamics model of metal spinning was established to investigate the effects of process parameters and temperature on the mechanical response, material flow, contact loading, and dislocation evolution behavior within the contact zone. The results indicate that the optimal deformation coordination is achieved with an arc radius of 25 Å, an indentation depth of 8 Å, and a tangential velocity of 1.5 Å/ps. Analysis of the normal and tangential forces shows that the normal load is rapidly established during the indentation stage, whereas the tangential load continuously increases with material shear transport. Both loads decrease significantly with increasing temperature. Elevated temperature effectively suppresses dislocation accumulation and simplifies the dislocation structure, causing the plastic deformation behavior to gradually transition toward a dominant primary slip-system mode. This study reveals the local deformation and dislocation evolution mechanisms during spinning and provides theoretical guidance for the process optimization of thin-walled spinning components.

Industrial robots are indispensable equipment in automated production lines and play a crucial role in advancing the development of intelligent manufacturing. Bearings are key components within robot joints. To ensure the precise execution of operational tasks and to prevent potential safety accidents in a timely manner, it is essential to perform fault diagnosis on the bearings within robot joints. However, fault diagnosis methods based on deep learning typically require a large amount of fault measurement data, which can be challenging to obtain due to various constraints. To address the issue of insufficient data, this paper proposes a fault diagnosis method based on the integration of digital twin technology and MTF-ResTLN. First, a digital twin model of the industrial robot is established, and fault excitations are injected into different nodes of the twin model to generate fault data under various node conditions. The measured data are then combined with the simulated fault data to form a training dataset. Furthermore, a novel classifier is developed by integrating the Markov Transition Field with a Residual Transfer Learning Network. It achieves cross-domain fault diagnosis and enhances the capability of fault diagnosis.

Automated Guided Vehicles (AGVs) play a crucial role in intelligent warehousing; however, effective path planning remains challenging because of obstacles, safety constraints, and the risk of suboptimal routes. This study proposes an improved Harris Hawks Optimization algorithm for AGV path planning, introducing strategies to enhance initial solution quality, balance global and local search, and avoid local optima. The proposed algorithm generates shorter, smoother, and safer paths, as demonstrated through benchmark tests, multi-scale grid-map simulations, and real-world AGV experiments. In terms of path length and computational efficiency, the enhanced algorithm significantly outperforms the original HHO, reducing average path length by 10.81% and average travel time by 11.94%. These results demonstrate that the proposed method provides a practical and reliable solution for autonomous warehouse navigation and significantly improves AGV path-planning performance.

Due to aging, high operational loads, and harsh working environments, industrial robots are prone to various failures. Predictive maintenance (PdM) offers a promising solution to this challenge. This study proposes a PdM method for industrial robots based on AM-CNN-BiGRU neural network. First, a convolutional neural network (CNN) and a bidirectional gated recurrent unit (BiGRU) are integrated to leverage the CNN’s ability to extract local spatial features and the BiGRU’s capability for learning global bidirectional temporal dependencies. This combination effectively captures the nonlinear and deep features within the data. Second, vibration, current, and torque data from the robot joints are input into the CNN as distinct channels, enabling multi-source data fusion. Concurrently, an attention mechanism (AM) is incorporated to assign adaptive weights to different data sources, enhancing the identification of critical fault signatures. Finally, ablation studies were conducted, and comparative experiments were performed between AM-CNN-BiGRU and several of the most representative and latest algorithms in the time series forecasting domain. During training, the Lion optimizer was employed for the deep learning models. The results demonstrate that AM-CNN-BiGRU achieved optimal performance across four evaluation metrics: R², MAE, MAPE, and RMSE, thereby validating its effectiveness and reliability.

To address the issue of real-time deformation monitoring for combined heading dies for titanium alloy bolts, this paper established a hardware system to introduce an indirect measurement method. In detail, we designed the monitoring model of different materials and developed a software system based on the model to monitor and analyze the die deformation during the heading process. The real-time data acquisition and experimental validation demonstrate the reliability of the system and the feasibility of the method.

The electric-tracked intelligent detection device is extensively used in engineering exploration and construction. However, in unstructured and complex terrains, its stability is challenged by a high center of gravity and poor road conditions, making the enhancement of its stability analysis crucial. This study is based on tracked vehicle models, constructing kinematic and dynamic equations for the device, and developing virtual and physical prototypes. Combining theoretical analysis, virtual simulations, and physical testing, the study analyzes motion parameters under typical conditions through numerical calculations and simulations. It examines the device’s dynamic characteristics during rapid turns in unstructured terrains. The research identifies variations in motion parameters, determines dynamic stability thresholds, and refines stability analysis methods applicable to the device, establishing evaluation criteria with multiple parameters and weight vectors. In conclusion, the developed models and prototypes help us understand the electric-tracked intelligent detection device’s dynamic characteristics and instability thresholds, enhancing its stability and operational efficiency and providing a theoretical foundation for related engineering machinery development.

Inspection robots, which improve hazard identification and enhance safety management, play a vital role in the examination of high-risk environments in many fields, such as power distribution, petrochemical, and new energy battery factories. Currently, the position precision of the robots is a major barrier to their broad application. Exact kinematic model and control system of the robots is required to improve their location accuracy during movement on the unstructured surfaces. By a virtual engine and digital twins, this study put forward a visualization monitoring and control system framework which can address the difficulties in the intelligent factories while managing a variety of data sources, such as virtual–real integration, real-time feedback, and other issues. To develop a more realistic dynamic model for the robots, we presented a neural-network-based compensation technique for the nonlinear dynamic model parameters of outdoor mobile robots. A physical prototype was applied in the experiments, and the results showed that the system is capable of controlling and monitoring outdoor mobile robots online with good visualization effects and high real-time performance. By boosting the positional accuracy of robots by 18% when navigating obstacles, the proposed precise kinematic model can increase the inspection efficiency of robots. The visualization monitoring and control system enables visual, digital, multi-method, and complete real-time inspections in high-risk factories, such as new energy battery factories, to ensure the safe and stable operations.

The curing process of hair-pin motor stator insulation is critical, as residual stress increases the risk of partial discharge and shortens a motor’s lifespan. However, studies on the stress-induced defects during insulation varnish curing remain limited. This research integrates three-dimensional numerical simulations and experimental analysis to develop a curing model based on unsaturated polyester imide resin, aiming to explore the mechanisms of residual stress formation and optimization strategies. A dual fiber Bragg grating (FBG) sensor system is employed for simultaneous temperature and strain monitoring, while curing kinetics tests confirm the self-catalytic nature of the process and yield the corresponding kinetic equations. The multi-physics simulation model demonstrates strong agreement with the experimental data. The results show that optimizing the curing process reduces the maximum stress from 45.1 MPa to 38.6 MPa, effectively alleviating the stress concentration. These findings highlight the significant influence of the post-curing temperature phase on residual stress. The proposed model offers a reliable tool for stress prediction and process optimization in various insulating materials, providing valuable insights for motor insulation system design.

Automated guided vehicles (AGVs) have emerged as crucial machinery in enterprise production and transportation as intelligent factories have gained traction. This study proposes a multi-source data enhancement fusion convolutional neural network (ME-CNN) approach for the mechanical fault diagnosis of heavy-duty AGVs. Specifically, an improved stacked autoencoder (SAE) was employed to augment the available data and address the issue of insufficient data samples during fault diagnosis. Subsequently, the augmented data were processed using a continuous wavelet transform (CWT). This method employs a combination of diverse measurement signals and data sources to facilitate a more precise and resilient diagnosis of heavy-duty AGVs. In addition, to further promote the coverage of smart factories, this method is combined with digital mirroring, and the virtual platform receives data to monitor the status of heavy-loaded AGVs in real-time and determine the type of fault. Following testing, the system response time was less than 500 ms, allowing for the timely diagnosis of faults. Finally, the validity of the method was verified by comparing a factory-heavy load AGV with various other algorithms, resulting in an accuracy rate of 97.0% for fault diagnosis.

This paper introduces an enhanced Whale Optimization Algorithm, named the Multi-Swarm Improved Spiral Whale Optimization Algorithm (MISWOA), designed to address the shortcomings of the traditional Whale Optimization Algorithm (WOA) in terms of global search capability and convergence velocity. The MISWOA combines an adaptive nonlinear convergence factor with a variable gain compensation mechanism, adaptive weights, and an advanced spiral convergence strategy, resulting in a significant enhancement in the algorithm’s global search capability, convergence velocity, and precision. Moreover, MISWOA incorporates a multi-population mechanism, further bolstering the algorithm’s efficiency and robustness. Ultimately, an extensive validation of MISWOA through “simulation + experimentation” approaches has been conducted, demonstrating that MISWOA surpasses other algorithms and the Whale Optimization Algorithm (WOA) and its variants in terms of convergence accuracy and algorithmic efficiency. This validates the effectiveness of the improvement method and the exceptional performance of MISWOA, while also highlighting its substantial potential for application in practical engineering scenarios. This study not only presents an improved optimization algorithm but also constructs a systematic framework for analysis and research, offering novel insights for the comprehension and refinement of swarm intelligence algorithms.