Anticipatory failure determination (AFD) is a failure prediction method used to identify deficiencies and hidden risks in product design during design or manufacturing stages, serving as a powerful tool to enhance product reliability. Current AFD mainly define scenarios of successful realization of product functions at a macro level and give insufficient attention to the underlying mechanisms that drive functional realization, which affects the rationality and accuracy of failure prediction. Furthermore, the existing studies for function reversal and standard-solution reversal predominantly adopt direct semantic reversal to generate failure scenarios, which leads to relatively simplistic reversal methods. To address these issues, firstly, a mechanism-based failure prediction approach was proposed. In this approach, the mechanism model of the product was extracted, the mathematical or physical expressions of the mechanism model were used to query potential failures, and the integrated mechanism model was simulated by Simulink software to effectively query failure modes of the product. Secondly, the reversal forms of functions and standard solutions were added based on the substance-field model, and a method for obtaining failure events by reverse application of inventive principles in TRIZ (Teoriya Resheniya Izobreatatelskikh Zadatch) was proposed, forming a new path to obtain failure events. Finally, an AFD prediction process combining mechanism models and the reverse application of TRIZ problem-solving tools was established, and the failure prediction for a passive power-damping boring bar was conducted to verify the feasibility of this method. The proposed method improves the acquisition way of failure scenarios based on the traditional AFD process, effectively enhancing the rationality and accuracy of failure mode prediction, which has significant theoretical insights and practical engineering value for improving product design reliability.

In order to improve the assembly accuracy of precision machine tool guideways, a matching design method for guideway geometric error shapes considering the pose error and assembly stress of the feed system is proposed. Firstly, a mapping model between the geometric errors of the guideways and the pose errors of the worktable was established based on the static equilibrium method. The geometric error shapes of the guideways were analyzed, and the matching experiments on the geometric error shapes were designed. Then, the CRITIC (criteria importance through inter-criteria correlation) weighting method was used to assign weights and evaluate the pose errors of the worktable. The comprehensive scores of the five errors under different combinations of guideway geometric error shapes were obtained, thus obtaining the optimal combinations of three sets of guideway geometric error shapes. On this basis, the stress model of rollers in the guideway slider was established using the Hertz contact theory, and the assembly stress under different combinations of guideway geometric error shapes was analyzed based on the information entropy theory. Finally, the combinations of guideway geometric error shapes that simultaneously met the optimal worktable pose error evaluation results and the most uniform distribution of assembly stress were screened out, and the optimum matching of guideway geometric error shapes considering the pose error and assembly stress of the feed system was obtained. The effectiveness of the proposed method was verified through experiments. The research results have important guiding significance for the precision design and guideway assembly error control of machine tools.

The engine cylinder block requires extremely high stability and precision in machining and assembly. The perpendicularity of the engine cylinder block thrust surfaces is an important index to evaluate the machining and assembly quality of the cylinder block. However, in the actual process of machining and assembly, due to the influence of various factors, the out-of-tolerance problem often occurs in the perpendicularity of cylinder block thrust surfaces. Therefore, an anti-deformation compensation control method to solve the problem of out-of-tolerance perpendicularity of engine cylinder block thrust surfaces is proposed. Firstly, a finite element model of the cylinder block and bearing cover after fastening was established, and the bolt tightening torque was identified as the main factor causing the deformation and out-of-tolerance perpendicularity of the thrust surface. Then, based on the three-coordinate measurement data of the thrust surface, the accuracy of the finite element model was verified, and an anti-deformation compensation machining method for the cylinder block thrust surface was proposed according to the bolt tightening torque-thrust surface deformation law. Finally, the feasibility of the compensation machining method was verified through the coordinate measurement data of the thrust surface in the actual machining and assembly experiment. In actual batch processing, the eccentric pin could be used instead of the traditional positioning method of "one side and two pins" to achieve the anti-deformation compensation machining of thrust surfaces. The result showed that the perpendicularity error of the engine cylinder block thrust surface after compensation was reduced by more than 78% compared with that before compensation, which proved the effectiveness of the proposed method. The research results provide a feasible solution for the machining deformation problem in precision manufacturing.

Lane line detection is a crucial part of autonomous driving technology. To address the problem of high false detection rate, as well as the difficulty in balancing frame rate and accuracy in the autonomous driving scenarios, an end-to-end GNN-Transformer detection framework was developed, in which graph neural network (GNN) was used to enhance the local geometric consistency of lane lines and a Transformer encoder-decoder was employed to complete the global dependency modeling and lane line prediction. In addition, learnable positional encoding was adopted and an optimized curve fitting strategy was introduced to improve the model's adaptability to complex scenarios. The proposed lane line detection method was experimentally verified on the TuSimple dataset, CULane dataset and CARLA simulator. Experimental results on the TuSimple dataset showed that the proposed method achieved a false detection rate of 0.019 2, which was reduced by up to 89% compared with other six methods, including ORANet. Meanwhile, the frame rate remained at 110 frames per second, indicating that the method achieved high detection accuracy, stability and real-time performance. Furthermore, the model was deployed on an RTRC4pro intelligent vehicle, thereby further evaluating the engineering application potential of the proposed method. The research results can provide strong support for the online perception of lane lines and its engineering applications in real-vehicle scenarios.

As a key piece of equipment in neurosurgical precision procedures, craniotomy robots have evolved in the technological framework from positioning tools to intelligent platforms, achieving significant progress in system architecture, control algorithm and mechanical design. This article reviewed the developmental history, analyzed the domestic and foreign status from the perspectives of technical capacity, industrial landscape and policy support, and compared the technical features and product characteristics of representative robot systems. It provided an in-depth analysis of the implementation approaches and latest advancements in core technologies, including mechanical configuration, path planning, control optimization, accuracy assurance and system integration. In clinical applications, the craniotomy robots had demonstrated advantages such as millimeter-level precision, standardized workflows and high safety in areas such as stereotactic biopsy, intracerebral hemorrhage puncture, while also showing potential for expansion into minimally invasive procedures. However, challenges remained in areas such as tactile feedback and real-time compensation for intraoperative brain tissue shift. Looking ahead, the neurosurgical paradigms will be driven toward greater safety, precision and intelligence through deeper integration of artificial intelligence, advancement of modular architectures, and the establishment of standardized training systems.

Trash cleaning in modern buildings mainly relies on manual labor, which suffers from high intensity and low efficiency. Meanwhile, general mobile robots face issues such as poor path smoothness and low computational efficiency in complex environments like narrow corridors. To address these issues, a building trash bin handling robot was designed. First, the mechanical structure of the robot was designed, consisting of a two-wheel differential drive chassis module, a lifting module, and a servo-driven clamping module to ensure the stable grasping of trash bins. Then, a hierarchical control system was built based on ROS and μC/OS. Furthermore, an improved A^* algorithm was proposed, which adopted a hierarchical directional neighborhood search strategy to optimize search efficiency, introduced an adaptive dual-weight heuristic function to avoid local optimal solutions, and utilized a key node extraction strategy to remove redundant nodes, thereby achieving path optimization. Simulation results showed that search time was reduced by up to 44%, and the number of search nodes was reduced by 50%- 80%. The designed robot successfully completed autonomous navigation, inspection, and trash bin handling tasks in prototype testing, verifying its feasibility and effectiveness in building environments.

Existing soft grippers commonly face the issue of limited contact area with objects, while structures capable of large-enveloping grasps are typically characterized by complex and bulky designs, making it difficult to reconcile the need for extensive surface contact with demands for structural simplicity and compactness. To address this trade-off, a novel origami gripper was designed. Drawing inspiration from the coiling motion of snakes, the gripper utilized a motor-driven winding mechanism to pull a string, driving the sequential folding of the origami structure, thereby achieving compliant coiling grasping. Experiments were conducted to analyze the influence of parameters including origami thickness, width, length, and the origami unit adjacent-edge angle on the gripping force. The results demonstrated that the gripper achieved optimal grasping performance when the origami thickness was 0.125 mm, the origami unit adjacent-edge angle was 60°, and the diagonal width was 22 mm. The gripper not only exhibited notable adaptability to both regular and irregular objects, but also demonstrated considerable load-bearing capacity, reliably grasping objects weighing up to 500 g. Characterized by its compact size, simple structure, large enveloping contact area, and substantial gripping strength, the proposed origami gripper provides an innovative technical solution for practical applications such as automated fruit and vegetable harvesting and compliant handling.

Based on the reliability analysis of aviation equipment with maintenance-free operating period (MFOP), the fault-free operation ability and task completion rate of the equipment can be improved by rationally planning the maintenance intervals. At present, the calculation of MFOP reliability indicators only considers the random uncertain factors based on specific distributions. However, for complex aviation equipment systems, it is difficult to obtain sample data of some uncertain factors, making it impossible to accurately establish probability distributions. Moreover, the reliability results obtained based on the ideally assumed probability distributions entail substantial deviations. Therefore, the probability-interval hybrid uncertainty was introduced into the MFOP reliability analysis framework. For the uncertain factors with sufficient sample sizes, they were characterized in a probabilistic way; for the uncertain factors with scarce sample sizes, they were characterized in an interval way. At the same time, taking into account the correlations among uncertain factors, a reliability analysis method for aviation equipment systems considering multi-source uncertainty was proposed. Finally, through an analytical example with four uncertain parameters and an engineering example of the water-landing airbag buffer system for aviation equipment involving six uncertain parameters, the effectiveness of the proposed method was verified. The research results provide a theoretical basis for maintenance decisions of aviation equipment.

Due to the dynamic and polymorphic characteristics of fault modes in train control center, traditional continuous-time Bayesian network fails to effectively address the the issue of polymorphism. Moreover, missing fault data in practical engineering makes it difficult to obtain accurate fault data.To overcome these challenges, this paper proposed a reliability analysis method integrating hyper-ellipsoidal T-S fault tree with Bayesian network.Firstly, the hyper-ellipsoid model was employed to constrain the probability interval of bottom events, in order to address the issue of data uncertainty.Secondly, a Bayesian network model was constructed based on the T-S fault tree, and the fuzzy numbers were used to characterize multiple fault states of the nodes. Finally, the proposed method was used to analyze the reliability of the train control center system. Through forward reasoning, the probability curves for each state during the system operation were obtained. Through posterior probability analysis, the vulnerable components of the system were identified.The research results demonstrated that compared with the conventional interval T-S fault tree, the analysis accuracy and reasoning capability of the proposed method had been improved. Additionally, the forward reasoning and posterior probability analysis results can provide theoretical support for the maintenance and reliability optimization of the train control center.

Considering that the scattered and bending states of anchor cables will affect the cutting operation of anchor withdrawing equipment on anchor cables, an anchor cable state recognition method based on image recognition was proposed. First, the YOLOv8 object detection algorithm was used to recognize the scattered states of the anchor cables. Then, the PSPnet semantic segmentation and image processing methods were employed to recognize the bending states of the anchor cables. The anchor cable scattered state recognition method based on YOLOv8 algorithm achieved a detection accuracy of over 0.900 for normal anchor cable, slightly scattered anchor cable and completely scattered anchor cable, indicating that the detection model had a good ability to recognize the scattered states of anchor cables. The anchor cable bending state recognition method based on PSPnet semantic segmentation and image processing could recognize the bending angles of normal anchor cable, overall bending anchor cable and partially bending anchor cable, providing a basis for the selection of anchor withdrawing equipment. By recognizing the scattered and bending states of anchor cables, the operation feasibility discrimination can be achieved, thereby providing theoretical support for the development of the anchor withdrawing robot.

With the accelerated intelligent transformation of the automotive industry, the wheel hubs, as the core safety components of vehicles, have increasingly higher requirements for detection accuracy and efficiency. However, the existing wheel hub detection methods are longer able to meet the demands for efficient and high-precision automatic detection. To address the shortcomings of high cost, low efficiency, and insufficient accuracy in existing wheel hub detection methods, a hybrid deep learning model (CNN-BiLSTM-AM) combining convolutional neural network (CNN), bidirectional long short-term memory (BiLSTM), and attention mechanism (AM) was proposed. A vehicle longitudinal control model was constructed to realize speed tracking, thereby achieving efficient and high-precision automatic wheel hub detection. When using BiLSTM for data modeling, this model employed CNN to suppress noise to solve the noise sensitivity defect of BiLSTM, and introduced AM to focus on critical information to avoid the issue of losing key features in simple models, thereby improving prediction accuracy. Simulation results showed that compared with traditional RNN (recurrent neural network), LSTM, GRU (gated recurrent unit), the CNN-BiLSTM-AM model achieved an average improvement of 3.17% in determination coefficient R² and an average reduction of 29.76% in MAE (mean absolute error) in the overall prediction task. Bench test results indicated that the proposed method could effectively complete wheel hub performance detection, achieving high-precision speed tracking while improving detection efficiency. This method provides an efficient solution for the automatic detection of vehicle wheel hubs.

The selection of motors directly affects the operational efficiency and reliability of the bearing life testing machine. Therefore, starting from the efficiency analysis of the crank-rocker mechanism—a key component of the bearing life testing machine, a precise motor selection method is proposed. Firstly, a method for calculating the average transmission efficiency of the crank-rocker mechanism based on compound Simpson's formula was constructed, and its effectiveness was verified through simulations. Then, a motor selection scheme that comprehensively considered the matching of parameters such as power, moment of inertia, and overload torque was proposed, completing the motor selection for the bearing life testing machine. Finally, the rationality of motor selection results was verified through prototype experiments. The results showed that the output torque of the bearing life testing machine remained stable under various radial loads, verifying that the motor selection results were reasonable and reliable. The proposed method can provide a reference for the motor selection of similar equipment.

To address the blockage issue of ship sea chests caused by marine biofouling, an active preventive control technology based on the 20 kHz ultrasonic cavitation effect is proposed. An ultrasonic anti-fouling device consisting of a transducer, an amplitude transformer and a vibration rod is designed, aiming to replace the traditional passive cleaning mode for mature biofilms and inhibit biofouling at the source. To further enhance the biofouling prevention performance, a barbell-shaped vibration rod composed of 18 serially connected units was developed. The synergistic vibration of these units expanded the sound field range and improved the uniformity and effective coverage of the sound field within the sea chest. Subsequently, an equivalent circuit method was employed to construct a full-link impedance matching model, and the mechanical impedances of the sandwich transducer, the stepped amplitude transformer and the barbell-shaped vibration rod were derived segment by segment. By designing parameters such as the cross-sectional ratio of the amplitude transformer and the unit length of the vibration rod, the impedances of all components were coupled and adapted through boundary conditions, which reduced energy reflection loss and realized the longitudinal resonance of the ultrasonic anti-fouling device, thereby improving the energy transmission efficiency. The multi-physics field coupling simulation results showed that the single-stage vibration rod generated a sound field with strong central intensity, weak edge intensity and obvious blind zones, while the barbell-shaped vibration rod formed a symmetric standing wave sound field with effective acoustic pressure at the edges. Its cavitation effect could disrupt the cell membranes of marine bio-eggs and planktonic larvae, thus blocking biofilm formation. Three groups of anti-fouling comparative experiments under simulated marine environments verified that the entire bottom of the chest in the blank control group was covered by organisms; the single-stage vibration rod group had a limited anti-fouling coverage, with obvious organism attachment at the edges of the chest bottom; more than 90% of the chest bottom area remained clean in the barbell-shaped vibration rod group, with only a small number of larvae attached in the corners. The designed ultrasonic anti-fouling device can realize non-contact and eco-friendly cleaning, which provides a new approach for the prevention of biofouling in ocean engineering equipment.

Aiming at the technical bottlenecks of geometric dimension redundancy and insufficient transmission ratio in traditional planetary reducers under high-speed and heavy-load conditions, an innovative design scheme for a differential planetary reducer is proposed, and its dynamics research is conducted. Firstly, the innovative characteristics of the differential planetary reducer were systematically elaborated, and its overall structural design was completed. On this basis, the multi-body dynamics modeling was performed using the ADAMS software, and virtual simulation experiments were employed to verify the feasibility of the reducer's transmission principle. Then, vibration tests were carried out to investigate the dynamic characteristics of the reducer. The test results demonstrated that the reducer exhibited a small vibration amplitude and excellent dynamic performance. Finally, the multi-objective particle swarm optimization algorithm was adopted to optimize the key parameters of the reducer gear transmission system. The optimized reducer achieved a significant improvement in comprehensive performance: the total mass was reduced by 4.2%, the root mean square of vibration acceleration was decreased by 23.4%, and the transmission efficiency was enhanced. Compared with the traditional planetary reducer, the novel differential planetary reducer possesses distinct advantages such as a larger transmission ratio and a more compact structure, which can provide a novel design approach for solving the engineering problem of insufficient torque in the transmission system of mechanical equipment under high-speed operating conditions.

In response to the application requirements of small joints with high-precision and heavy-load in service robots, a cycloidal-pin-annulus reducer was designed, and its transmission performance was verified. Based on the requirements of the working conditions, the parameter calculation, structural design and the comprehensive performance testing platform for the reducer were completed. Through high-torque acceleration failure tests, combined with dynamics finite element analysis, microtopography observation and elemental-mapping analysis, the failure mechanism of the reducer was systematically studied. Based on the failure mechanism, the reducer was improved, and the performance comparison tests before and after improvement were carried out. The failure analysis indicated that the main failure modes of the reducer included the adhesion wear on the surface of the eccentric shaft and the inner bore of the cycloidal wheel, the adhesion wear and local pitting of the needle roller bearing, the wear of the cage accompanied by surface oxidation, as well as the loosening and fracture of the reamed hole screw, and the deformation of the flanges. The lightweighting and performance improvement of the reducer were achieved by adjusting the installation direction of the reamed hole screw, improving the structure of the output flange, and reducing the number of gear pin. The torque density increased from 124.38 N·m/kg to 130.01 N·m/kg. The transmission error decreased from approximately 7 arc·min to 5 arc·min. The maximum transmission efficiency under the rated torque increased from 62.87% to 81.59%. After 30 minutes of no-load operation, the highest housing temperature decreased from 46 ℃ to 35 ℃. The research results provide valuable insights for the development of robotic joint modules towards compact, high-precision and heavy-load.

To address the issue of stress concentration in hydraulic slips during the gripping of drill pipes in deep well drilling, which often leads to drill pipe damage, a hybrid optimization method integrating Bayesian neural network (BNN) and artemisinin slime mold algorithm (ASMA) is proposed. Taking the hydraulic slip with flat-top tooth structure as the research object, a slip tooth-drill pipe contact model was first established, and the stress distribution of the slip tooth and the drill pipe was calculated through finite element analysis to extract the initial dataset. On this basis, orthogonal experiments were conducted to screen sensitive parameters, and the sample dataset was further expanded for surrogate model training. Subsequently, a BNN-based surrogate model was developed to fit the slip tooth profile parameters and mechanical responses (with a determination coefficient of R²>0.95), followed by multi-objective optimization utilizing the ASMA. The results demonstrated that the maximum equivalent stress of the slip tooth was reduced from 582.96 MPa to 303.53 MPa (a reduction of 47.9%), while the maximum equivalent stress of the drill pipe decreased from 360.03 MPa to 235.87 MPa (a decrease of 34.5%), significantly enhancing the performance of the slip tooth. The research results provide an efficient and reliable novel approach for the structural optimization of hydraulic slip tooth profiles.