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The fracture phenomenon of fastener clips occurs frequently in the sections with serious rail corrugation of high-speed railway, which brings huge potential safety hazards to track service and driving safety. In order to prevent the vibration fatigue fracture of fastener clips in high-speed railway, a dynamic vibration absorber (DVA) for clips is proposed in this paper to suppress the primary sensitive frequency of the corresponding resonant of clips. Firstly, based on the optimal design theory of DVA, a parameters design method of ring-shaped DVA suitable for clips is proposed. Secondly, the vibration characteristics of the high-speed railway fastener system are investigated, and the main reason for the fastener clips fracture is analyzed by simulation analysis combined with filed test. Then, focusing on the primary resonant frequency of clips, the vibration reduction effect of the ring-shaped DVA is analyzed. The results show that the ring-shaped DVA can change the natural frequency of the clip system, reduce the vibration response in the range of the original resonant frequency, and avoid the resonance phenomenon. Finally, the influences of mass ratio, frequency ratio, and damping ratio on the vibration reduction effect are analyzed, and the effectiveness of the parameters design method of ring-shaped DVA proposed in this paper is further proved. The research results can provide methodological support for the anti-breakage design of high-speed railway fastener clips. [ABSTRACT FROM AUTHOR]

Traveling by high-speed rail and railway transportation have become an important part of people's life and social production. Track is the basic equipment of railway transportation, and its performance directly affects the service lifetime of railway lines and vehicles. The anomaly detection of rail fasteners is in a priority, while the traditional manual method is extremely inefficient and dangerous to workers. Therefore, this paper introduces efficient computer vision into the railway detection system not only to locate the normal fasteners, but also to recognize the fasteners states. To be more specific, this paper mainly studies the rail fastener detection based on improved You can Only Look Once version 5 (YOLOv5) network, and completes the real-time classification of fastener states. The improved YOLOv5 network proposed contains five sections, which are Input, Backbone, Neck, Head Detector and a read-only Few-shot Example Learning module. The main purpose of this project is to improve the detection precision and shorten the detection time. Ultimately, the rail fastener detection system proposed in this paper is confirmed to be superior to other advanced algorithms. This model achieves on-line fastener detection by completing the "sampling-detection-recognition-warning" cycle of a single sample before the next image is sampled. Specifically, the mean average precision of model reaches 94.6%. And the model proposed reaches the speed of 12 ms per image in the deployment environment of NVIDIA GTX1080Ti GPU. [ABSTRACT FROM AUTHOR]

The paper presents comparative laboratory investigations of tension fastener models that attach rails to concrete sleepers. The aim of the paper was to assess the influence of geometric nonconformity of the actual industrial product that meets the PKP Polskie Linie Kolejowe S.A. requirements on the operation of a tension-fastening clamp under stress. Due to the cost and limited possibility of research of the actual industrial product, an additional objective of the research was to validate the usefulness of it in the comparative assessment of the models. In the research, the authors used models of tension fastening clamps manufactured in incremental engineering technology (3D print) on scale 1:2. The properties of the fastening clamps (in their nominal shape) were compared (described in the PKP Polskie Linie Kolejowe S.A. documentation) with the fastening clamps of the actual shape. In the investigation, the authors have confirmed the negative influence of the non-conformance of the shape of the actual fastening clamps with the nominal ones. [ABSTRACT FROM AUTHOR]

With the increase of the running time of the subway system, long-term use will lead to the reduction of damping, the change of the operating state of the wheels and the track, and the effect of vibration damping measures will gradually weaken. In order to maintain a low level of environmental vibration in vibration sensitive areas, the reconstruction of some existing lines with high level vibration is imminent. In order to solve this problem, this paper focuses on the reconstruction of the existing line of longitudinal sleeper track, which is commonly used in the middle and high level vibration reduction measure in the subway. Because of the high cost of reconstruction of the slab and its lower part, this paper tries to realize the reconstruction of the existing longitudinal sleeper track by changing the fastener system. In this paper, the environmental vibration prediction model of longitudinal sleeper track is established by using the theory of infinite long period structure and the 2.5-dimensional finite element and boundary element (2.5D FE-BE) environmental vibration prediction model. The validity of the numerical model is determined by comparing the field experiment with the numerical simulation. On the basis of the above, the stiffness matching of longitudinal sleeper and fastener is explored by using the verified numerical model. The reasons for the poor vibration reduction effect of the existing longitudinal sleeper matching floating rail fastener (low stiffness) are analyzed. And the matching strategy of the supporting stiffness for vibration reduction of longitudinal sleeper and fastener are summarized. [ABSTRACT FROM AUTHOR]

A rod fastened rotor (RFR), which contains serial disks, rods (or a rod), and a torque tube, is the main form of heavy duty gas turbines and light duty aero engines. The roughness of the interface is the main parameter affecting the bending stiffness of RFR, and the jumpiness of the disks is the intuitive representation of the unbalance of the rotor. In this paper, a multi-optimization algorithm, NSGA-Ⅱ, was used to improve the vibration behavior of the RFR-bearing system. First, the quantitative relation between the bending stiffness of the rotor and the roughness of the interface was set up by combining the Persson contact theory and the Greenwood–Williamson model, and the nondimensional bending stiffness (NS) was proposed as the optimization objective of roughness. Then, the optimization objective of the jumpiness of the disk, (A + B), was rendered based on the minimum unbalance force. Finally, the multi-optimization on the roughness and jumpiness of the RFR was carried out, and the vibration behavior was improved greatly. In addition, this multi-optimization method was applied to a practical RFR, and the experimental results showed that the vibration amplitude was reduced by about 82%. The work in this paper can provide the guidance for vibration reduction of the rotor system. [ABSTRACT FROM AUTHOR]

Unmanned Aerial Vehicle (UAV) inspection has gradually replaced manual inspection of transmission tower, which produces many images. While it is laborious and time-consuming to manually analyze these images, there are also challenges in automatically detecting small-size defective components such as bolts in transmission tower images, due to problems including complex background, small size, and many similar objects of bolts. In this paper, by virtue of multi-scale features and context information, we propose a deep neural network named Camp-Net (Context Information and Multi-Scale Pyramid Network) to identify bolts defect in transmission tower images. First, multi-scale feature fusion combines deep features and shallow features in convolutional networks to detect small-size bolts. Second, context information fusion puts the information around bolts into the detection network to remove the disturbance of complex background and similar objects. An image dataset containing defective bolts and normal bolts is constructed for model training and testing. Experimental results show that bolts with loose pins and bolts without pins among fittings in transmission tower can be accurately identified with the proposed model. The Average Precision (AP) of defective bolts detection of this model can be 11.4% higher than that of the commonly used high performance model, Faster R-CNN. [ABSTRACT FROM AUTHOR]

Hanging sleepers, which result from the differential settlement of the ballast layer, are a prevalent issue that leads to the rapid degradation of track components that include the ballast, sleeper, fastener, and rail. A novel type of sleeper, the wedge-shaped self-leveling sleeper (WSS), was proposed as a solution to the hanging sleeper problem. The WSS leverages the train's dynamic loading and the gravity of the ballast to naturally allow the ballast particles to fill the gap between the ballast and the sleeper. This paper focuses on the dynamic performance of the WSS from different aspects, which include wedge angles (30°, 45°, and 60°), sleeper materials (concrete and plastic), and the number of WSS to replace regular sleepers in the transition zone. A series of numerical modeling [which coupled multibody simulation (MBS) and discrete-element methods (DEM)] were conducted to design, optimize, and test the WSS. The results reveal that a concrete WSS is well-suited to address the problem of hanging sleepers in transition zones. The WSS with a 45° angle demonstrated superior performance compared with other types of WSS. Importantly, the WSS could reduce vibrations in the vehicle and track, even when dealing with hanging sleepers. Due to the self-leveling function, the WSS shows significant promise for applications in transition zones, which could reduce the frequent need for track geometry maintenance. [ABSTRACT FROM AUTHOR]

The vibration and transmission characteristics of track components during vehicle operation is a complex issue. To address this issue, this paper proposes a flexible wheelset-damped track-tunnel-soil model that considers the interaction between the flexible wheelset and the rail. Using field measurements and the developed model, this study investigates the vibration characteristics of track components and explores the influence of fastener stiffness and damping pad stiffness on track vibration and vibration transfer characteristics between components from an energy perspective, using track spectrum power flow as the evaluation index. The results demonstrate that the model considering wheel-rail coupling can more comprehensively reflect the vibration characteristics of the track during train operation. Compared to fasteners with lower stiffness, damping pads with lower stiffness can reduce track vibration in a wider frequency range. Lower fastener stiffness reduces the transmission of vibration in 50∼2000 Hz, while lower damping pad stiffness reduces the transmission of vibrations in 1∼2000 Hz. [ABSTRACT FROM AUTHOR]

Rack railway is a rail transportation that utilizes the gear-rack engagement to supply the traction forces on large-slope lines, primarily employed in mountainous regions where the conventional trains cannot operate due to the wheel-rail adhesion limitations. However, there is limited research on this system dynamics and further analysis is required to understand the interaction mechanism between the rack vehicle and track. Based on the typical vehicle-track coupled dynamics theory, the rack vehicle-track spatially coupled dynamics model is developed in this paper to evaluate the dynamic behaviours of the rack vehicle and the track system running on large-slope line with a detailed modeling of the longitudinal motion for the rack and sleeper, where the time-varying gear-rack mesh stiffness and the rail geometrical irregularity are incorporated. The excellent agreement observed between simulated and field-test results serves as strong validation for developed dynamics model. The results demonstrate that the vibration responses of the wheelset and the rack are dominated by the gear-rack mesh excitations. It should be noticed that the wheel loads are reduced by the presence of the gear-rack engagement. Furthermore, the vibration of the sleeper can explicitly reflect the gear-rack mesh characteristics through the transfer of the rack fastener. [ABSTRACT FROM AUTHOR]

Although bolted joints may appear simple and are easy to manipulate, they are challenging to model and analyze due to their complex structural patterns and statically indeterminate nature. Ensuring the structural integrity of these joints requires maintaining proper bolt preload and clamping force, which is crucial for preventing failures such as overload, excessive bearing stress, fatigue, and stripping caused by seizing or galling. Achieving the necessary clamping force involves carefully controlling the input tightening torque, which is divided into the pitch torque and the friction torques at the bolt or nut bearing surfaces and in the engaged threads. The resulting clamping force is critical for generating the required force within the bolt. However, the achieved bolt force depends on several factors, such as friction at the joint's contact surfaces, grip length, and the relative rotation between the bolt and nut during tightening. Friction at the contact surfaces, particularly beneath the bolt head or nut and between the threads, consumes a significant portion of the applied tightening torque—approximately 90%. This paper explores the three existing bolt internal pitch, bearing, and thread friction torques that are generated by the external applied torque in a bolted joint, as well as their contributions and variations throughout a loading cycle composed of three phases: tightening, settling, and untightening. An analytical model is developed to determine these torque components, and its results are compared with those obtained from finite element (FE) modeling and experimental testing from previous studies. Finally, this study examines the torque–tension relationship during bolt tightening, offering insights into the required accuracy of bolt and clamped member stiffness. The bolt samples used in this study include M12 × 1.75 and M36 × 4 hex bolts. [ABSTRACT FROM AUTHOR]

Short pitch corrugation has been a problem for railways worldwide over one century. In this paper, a parametric investigation of fastenings is conducted to understand the corrugation formation mechanism and gain insights into corrugation mitigation. A three-dimensional finite element vehicle–track dynamic interaction model is employed, which considers the coupling between the structural dynamics and the contact mechanics, while the damage mechanism is assumed to be differential wear. Various fastening models with different configurations, boundary conditions, and parameters of stiffness and damping are built up and analysed. These models may represent different service stages of fastenings in the field. Besides, the effect of train speeds on corrugation features is studied. The results indicate: (1) Fastening parameters and modelling play an important role in corrugation formation. (2) The fastening longitudinal constraint to the rail is the major factor that determines the corrugation formation. The fastening vertical and lateral constraints influence corrugation features in terms of spatial distribution and wavelength components. (3) The strengthening of fastening constraints in the longitudinal dimension helps to mitigate corrugation. Meanwhile, the inner fastening constraint in the lateral direction is necessary for corrugation alleviation. (4) The increase in fastening longitudinal stiffness and damping can reduce the vibration amplitudes of longitudinal compression modes and thus reduce the track corrugation propensity. The simulation in this work can well explain the field corrugation in terms of the occurrence possibility and major wavelength components. It can also explain the field data with respect to the small variation between the corrugation wavelength and train speed, which is caused by frequency selection and jump between rail longitudinal compression modes. [ABSTRACT FROM AUTHOR]

Many compartments are prone to pose safety hazards such as loose fasteners or object intrusion due to their confined space, making manual inspection challenging. To address the challenges of complex inspection environments, diverse target categories, and variable scales in confined compartments, this paper proposes a novel GMS-YOLO network, based on the improved YOLOv8 framework. In addition to the lightweight design, this network accurately detects targets by leveraging more precise high-level and low-level feature representations obtained from GhostHGNetv2, which enhances feature-extraction capabilities. To handle the issue of complex environments, the backbone employs GhostHGNetv2 to capture more accurate high-level and low-level feature representations, facilitating better distinction between background and targets. In addition, this network significantly reduces both network parameter size and computational complexity. To address the issue of varying target scales, the first layer of the feature fusion module introduces Multi-Scale Convolutional Attention (MSCA) to capture multi-scale contextual information and guide the feature fusion process. A new lightweight detection head, Shared Convolutional Detection Head (SCDH), is designed to enable the model to achieve higher accuracy while being lighter. To evaluate the performance of this algorithm, a dataset for object detection in this scenario was constructed. The experiment results indicate that compared to the original model, the parameter number of the improved model decreased by 37.8%, the GFLOPs decreased by 27.7%, and the average accuracy increased from 82.7% to 85.0%. This validates the accuracy and applicability of the proposed GMS-YOLO network. [ABSTRACT FROM AUTHOR]

The aim of the present paper is to investigate the influence of boundary conditions of rigid fastening on the boundaries of regions of dynamical instability of closed shells of revolution, close by their forms to cylindrical ones, with an elastic filler. It is assumed that a shell is under the action of external pressure which varies in time and temperature. We consider the shells of average length whose shape of generatrix of the middle surface is a parabolic function. We consider the shells of positive and negative Gaussian curvature. The formulas are obtained for finding eigenfrequencies and boundaries of regions of dynamical instability depending on the boundary conditions, Gaussian curvature, initial stressed state, temperature and amplitude of a shell deviation from cylindrical form. The focus is on finding the moot dangerous area of dynamical instability and on the lowest eigenfrequencies of shells under consideration. [ABSTRACT FROM AUTHOR]

The sustainability of the manufacturing industry is of special importance to increase the protection of the environment. The production of fasteners like self-piercing rivets, however, is costly, time-consuming and energy-intensive. The heat treatment and the coating, which are mandatory in conventional self-piercing rivets to achieve adequate strength, ductility and corrosion resistance, are especially crucial in this respect. Within this paper, an approach for an increase in the sustainability in fastener production is presented. The use of alternative, high strain hardening stainless steels as rivet material enables a shortening of the process chain, because post treatment of the rivets after they are formed can be omitted. As the change in rivet material and processing causes some issues along the process chain, the focus of this paper is on the holistic evaluation of the challenges within the forming of high strain hardening steel and the impact of the changed rivet properties on the joining result. [ABSTRACT FROM AUTHOR]

In mechanical systems, various threaded fasteners are widely used to connect separated components. Loosening occurs frequently due to exposure to harsh working environments, especially vibration environments. Loosening detection can eliminate hidden dangers in advance, and its importance cannot be overemphasized. Ultrasonic detection methods based on machine learning have become popular. However, a proposed machine learning model in a published paper is only suitable for a specific mechanical structure with threaded fasteners; the generality of this type of model is poor. In addition, existing models are well-trained based on abundant labeled data. The labeled data are commonly sparse in practical engineering applications, and it is labor-consuming to obtain these data. This paper presents the creation of a generalized detection model suitable for different threaded connection structures based on transfer learning and the exploitation of a small amount of labeled data for model training and accurate loosening detection. A transfer learning network named the cross-domain matching-mix transfer network (CDMTNet) suitable for different mechanical structures was proposed to transfer the knowledge in a source structure to a target structure. The CDMTNet consists of a feature extraction module, a feature disentangling module, and a classification module. The matching-mix method for data augmentation was proposed to finely train the CDMTNet by exploiting only a small amount of labeled data in the target structure. Two different mechanical structures with threaded fasteners (i.e., a lap joint and a globe-cone joint) were tested in the experiment. Their detection accuracies were both greater than 0.8. The results indicated that our method had wide application potential in engineering. [ABSTRACT FROM AUTHOR]

Rails play a vital role in the bearing and guidance of high-speed trains, and the normal condition of rail components is the guarantee of the operation and maintenance safety. Fasteners are critical components for fixing the rails, so it is particularly important to detect whether they are in a normal state or not. The current rail-fastener detection models have some drawbacks, including poor generalization ability, large model volume and low detection efficiency. In view of this, an improved YoLoX-Nano rail-fastener-defect-detection method is proposed in this paper. The CA attention mechanism is added to the three output feature maps of CSPDarknet and the enhanced feature extraction part of the Path Aggregation Feature Pyramid Network (PAFPN); the Adaptively Spatial Feature Fusion (ASFF) is added after the PAFPN output feature map, which enables the semantic information of the high-level features and the fine-grained features of the bottom layer to be further enhanced. The improved YoLoX-Nano model has improved the AP value by 27.42% on fractured fasteners, 15.88% on displacement fasteners and 12.96% on normal fasteners. Moreover, the mAP value is improved by 18.75%, and it is 14.75% higher than the two-stage model Faster-RCNN on mAP. In addition, compared with YoLov7-tiny, the improved YoLoX-Nano model achieves 13.56% improvement on mAP. Although the improved model increases a certain amount of calculation, the detection speed of the improved model has been increased by 30.54 fps and by 32.33 fps when compared with that of the Single-Shot Multi-Box Detector (SSD) model and the You Only Look Once v3 (YoLov3) model, reaching 54.35 fps. The improved YoLoX-Nano model enables accurate and rapid identification of the defects of rail fasteners, which can meet the needs of real-time detection. Furthermore, it has advantages in lightweight deployment of terminals for rail-fastener detection, thus providing some reference for image recognition and detection in other fields. [ABSTRACT FROM AUTHOR]

Threaded fasteners are prevalent throughout modern manufacturing. Thus, as the demand for automation in manufacturing increases, so does the demand for automated threaded fastening systems. However, many fundamental issues and engineering challenges still hinder robustness in automation, particularly for smaller screws and critical product finishing requirements. This paper surveys the state of the art in threaded fastening automation and discusses open questions for further research. This survey covers the following areas: 1) fundamentals of threaded fastening, including basic concepts and definitions; 2) analysis of the entire assembly process (consisting of part feeding and orientation, pickup, alignment, and driving), including discussions of tools, control strategies, and other considerations; 3) failure modes and techniques to mitigate them; 4) threaded fastening systems and electromechanical approaches; and 5) open challenges and suggestions for future development. Understanding the current state of automation in threaded fastening will provide a foundation for researchers to advance this field. Note to Practitioners—This paper is motivated by the problem of an automated assembly of small screws, one of the most challenging problems in a smartphone assembly. It represents a rigorous review of robotic screwdriving literature to identify the state-of-the-art and open problems. The review material was targeted toward engineers working on related problems with the sponsor and has proved useful to them. To benefit researchers in the field of robotic and automated assembly, we have compiled the review material in the form of a survey paper. This paper covers theoretical fundamentals, tools, control and failure detection strategies, industrial applications, and open problems for robotic screwdriving. It provides a foundation for readers to familiarize themselves with the state of the art and conduct further research on this thread. [ABSTRACT FROM AUTHOR]

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Rail-fastening components are essential for ensuring the safety of urban rail systems by securing rails to sleepers. Traditional inspection methods rely heavily on manual labor and are inefficient. This paper introduces a novel approach to address these inefficiencies and the challenges faced by computer vision-based inspections, such as missed detections due to imbalanced samples and limitations in conventional image segmentation techniques. Our approach transitions the industry's focus from qualitative to a more precise quantitative analysis of rail-fastening components. We propose Mask-FRCN, an advanced image segmentation network that incorporates three key technological enhancements: the fully refined convolutional network module (FRCN),which refines the segmentation boundaries for SFC-type fasteners; the Channel-WiseKnowledge Distillation (CWD) algorithm, which boosts the model's inference efficiency; and the FCRM methodology, which enhances the extraction capabilities for features specific to SFC-type fasteners. Furthermore, we introduce a fastener system inspection and quantization method based on the Mask FRCN method (FIQ), a novel technique for quantifying the condition of components by using image features, template matching with random forests, and a clustering calculation method derived from segmentation results. Experimental results validate that our method significantly surpasses existing techniques in accuracy, thereby offering a more efficient solution for inspecting rail-fastening components. The enhanced Mask-FRCN achieves a segmentation accuracy of 96.01% and a reduced network size of 36.1 M. Additionally, the FIQ method improves fault detection accuracy for SFC-type fasteners to 95.13%, demonstrating the efficacy and efficiency of our innovative approach. [ABSTRACT FROM AUTHOR]

Fastener screws are critical components of rail fasteners. For the fastener screw maintenance robot, an image-based fast fastener screw detection method is urgently needed. In this paper, we propose a light-weight model named FSS-YOLO based on YOLOv5n for rail fastener screw detection. The C3Fast module is presented to replace the C3 module in the backbone and neck to reduce Params and FLOPs. Then, the SIoU loss is introduced to enhance the convergence speed and recognition accuracy. Finally, for the enhancement of the screw detail feature fusion, the shuffle attention (SA) is incorporated into the bottom-up process in the neck part. Experiment results concerning CIoU and DIoU for loss, MobileNetv3 and GhostNet for light-weight improvement, simple attention mechanism (SimAM), and squeeze-and-excitation (SE) attention for the attention module, and YOLO series methods for performance comparison are listed, demonstrating that the proposed FSS-YOLO significantly improves the performance, with higher accuracy and lower computation cost. It is demonstrated that the FSS-YOLO is 7.3% faster than the baseline model in FPS, 17.4% and 19.5% lower in Params and FLOPs, respectively, and the P, mAP@50, Recall, and F1 scores are increased by 10.6% and 6.4, 13.4%, and 12.2%, respectively. [ABSTRACT FROM AUTHOR]

Changes in the geological environment and track wear, and deterioration of train bogies may lead to the looseness of subway fasteners. Identifying loose fasteners randomly distributed along the subway line is of great significance to avoid train derailment. This paper presents a convolutional autoencoder (CAE) network-based method for identifying fastener loosening features from the distributed vibration responses of track beds detected by an ultra-weak fiber Bragg grating sensing array. For an actual subway tunnel monitoring system, a field experiment used to collect the samples of fastener looseness was designed and implemented, where a crowbar was used to loosen or tighten three pairs of fasteners symmetrical on both sides of the track within the common track bed area and the moving load of a rail inspection vehicle was employed to generate 12 groups of distributed vibration signals of the track bed. The original vibration signals obtained from the on-site test were converted into two-dimensional images through the pseudo-Hilbert scan to facilitate the proposed two-stage CAE network with acceptable capabilities in feature extraction and recognition. The performance of the proposed methodology was quantified by accuracy, precision, recall, and F1-score, and displayed intuitively by t-distributed stochastic neighbor embedding (t-SNE). The raster scan and the Hilbert scan were selected to compare with the pseudo-Hilbert scan under a similar CAE network architecture. The identification performance results represented by the four quantification indicators (accuracy, precision, recall, and F1-score) based on the scan strategy in this paper were at least 23.8%, 9.5%, 20.0%, and 21.1% higher than those of the two common scan methods. As well as that, the clustering visualization by t-SNE further verified that the proposed approach had a stronger ability in distinguishing the feature of fastener looseness. [ABSTRACT FROM AUTHOR]

Cold-formed steel (CFS) elements have gained significant attention in the field of structural engineering due to their numerous advantages, including high strength-to-weight ratio, cost-effectiveness, and ease of assembly and prefabrication. This review paper presents a comprehensive state-of-the-art analysis of the design and analysis of CFS structures, with a specific focus on columns and beams. The primary objectives and aims of this review paper are to provide a detailed assessment of the factors influencing the behavior and performance of CFS elements, including partial composite action, fastener spacing, bolt arrangement, web aperture, stiffeners, and connection spacing, to propose and present various formulas and methodologies that accurately estimate critical buckling loads, strength, and moment resistance for CFS members, and to emphasize the significance of proper screw and bolt placement in preventing premature failure and enhancing the overall load-carrying capacity of CFS structures. Additionally, the impact of temperature on the mechanical properties and performance of CFS members is discussed. The review paper proposes different formulas and methodologies to accurately estimate critical buckling loads, strength, and moment resistance for CFS members. Moreover, the paper highlights the importance of proper screw and bolt placement to prevent early failure and improve the overall load-carrying capacity of CFS structures. The discussion also emphasizes the need for revisions in existing standards and codes to provide more practical guidelines for designers and engineers. Overall, this state-of-the-art review paper provides valuable insights and recommendations for researchers and practitioners involved in the design and analysis of CFS elements. [ABSTRACT FROM AUTHOR]

This paper investigates an approach that uses the cognitive architecture Soar to improve the performance of an automated robotic system, which uses a combination of vision and force sensing to remove screws from laptop cases. Soar’s long-term memory module, semantic memory, was used to remember pieces of information regarding laptop models and screw holes. The system was trained with multiple laptop models and the method in which Soar was used to facilitate the removal of screws was varied to determine the best performance of the system. In all the cases, Soar could determine the correct laptop model and in what orientation it was placed in the system. Soar was also used to remember what circle locations that were explored contained screws and what circles did not. Remembering the locations of the holes decreased a trial time by over 60%. The system performed the best when the number of training trials used to explore circle locations was limited, as this decreased the total trial time by over 10% for most of the laptop models and orientations. Note to Practitioners—Although the amount of discarded electronic waste in the world is rapidly increasing, efficient methods that can handle this in an automated non-destructive fashion have not been developed. Screws are a common fastener used on electronic products, such as laptops, and must be removed during nondestructive methods. In this paper, we focus on using the cognitive architecture Soar to facilitate the disassembly sequence of removing these screws from the back of laptops. Soar is able to differentiate between different models of laptops and store the locations of screws for these models leading to an improvement of the disassembly time when the same laptop model is used. Currently, this paper only uses one of Soar’s long-term memory modules (semantic memory) and a screwdriver tool. However, this paper can be extended to use multiple tools by using different features available in Soar such as other long-term memory modules and substates. [ABSTRACT FROM AUTHOR]

Missing fastener detection is a critical task due to its similar characteristics with surrounding environments. In this paper, a machine vision based fully automatic detection and classification of missing fastener detection system is proposed using Support Vector Machine (SVM) classifier. This proposed system consists of preprocessing, transformation, feature extraction and classifications. Image resizing is performed as preprocessing step and Gabor transform is used as transformation technique. Grey Level Cooccurence Matrix (GLCM) features, Local Binary Pattern (LBP) features and Discrete Wavelet Transform (DWT) are used as features in this paper. SVM classifier is used as classifier in order to classify the test rail track image into either track image with fastener or track image without fastener. [ABSTRACT FROM AUTHOR]

Defect detection of track fasteners is a prerequisite for safe and reliable railroad operation. The traditional manual visual inspection method has been unable to meet the growing demand for railroad network inspection in China. To achieve the need for accurate, fast, and intelligent detection of rail fasteners, this paper proposes a rail fastener defect detection model based on improved YOLOv5s. Firstly, the convolutional block attention module (CBAM) is added to the Neck network of the YOLOv5s model to enhance the extraction of essential features by the model and suppress the information of minor features. Secondly, a weighted bidirectional feature pyramid network (BiFPN) is introduced to realize the multi-scale feature fusion of the model. Finally, the K-means++ algorithm is used to re-cluster the dataset to obtain the anchor box suitable for the fastener dataset and improve the positioning ability of the model. The experimental results show that the improved model achieves an average mean precision (mAP) of 97.4%, a detection speed of 27.3 FPS, and a model memory occupancy of 15.5 M. Compared with the existing target detection model, the improved model has the advantages of high detection accuracy, fast detection speed, and small model memory occupation, which can provide technical support for edge deployment of rail fastener defect detection. [ABSTRACT FROM AUTHOR]

This paper presents test results from a study on the locking performance of jack bolt nuts with and without added secondary locking. A Junker-type test apparatus is used to apply dynamic transverse force loading to induce loosening. The test data reveal there are two mechanisms of loosening for jack bolt nuts, namely slip of the nut body and slip of the jack bolts. Furthermore, the data show the dominant mechanism of loosening in jack bolt nuts is nut body slip. Since the only currently available option for secondary locking for these types of fasteners is holes in the jack bolts heads for lockwire, additional methods for secondary locking for both mechanisms of loosening are developed and tested. For the test parameters used in this study, it is found that even without any secondary locking, loosening occurs only at relatively low preload levels. The effect of introducing secondary locking to only the jack bolts is a modest decrease in the rate of loosening. The effect of adding secondary locking to only the main nut body is a significant decrease in the rate of loosening. Adding mechanical or adhesive secondary locking to the main nut body and mechanical locking to the jack bolts eliminates loosening even at low preload levels. [ABSTRACT FROM AUTHOR]

Modeling of the structural behavior of the housing of an electromagnetic railgun is usually carried out without considering the electromagnetic origin of the forces; the magnetic pressure acting on the rails is assumed to act at the rail surfaces only. This approximation proves to be a great challenge, as moving pressure profiles at speeds of a railgun projectile are very rare in purely mechanical investigations. In the past, we have published several papers on the structural mechanics of railgun housings characterized by discrete supports. We could show that the displacement of rail surfaces at the position of the armature can reach amplitudes that are not negligible. In fact, in the case of metal brush armatures, the increase in distance between the rail surfaces of only tenths of a millimeter can make the difference between the solid contact and arcing to increase. We also matched our findings to experimental data that were, however, so far only gained in static tests. In this paper, we analyze the foundations of our mechanical modeling using electromagnetic calculations. We calculate a 3-D distribution of the $J \times B$ volume force density in the rails and use them as input for our structural mechanics model. A second point to be addressed concerns the parts made from conducting materials, such as our discrete supports made from steel bolts, which are situated in rapidly changing magnetic fields during the experiments. Again, the influence on our structural mechanics modeling is discussed. [ABSTRACT FROM AUTHOR]

This paper investigates the performance of a magnetic lead screw (MLS) equipped with skewed arc magnets. Skewed arc magnets are adopted to approximate the helical ones, because the latter is hard to realize. Although similar method had been proposed in the previous literature, discussion about the parameters of the skewed arc magnets was insufficient and little attention was paid to the gear ratio of the resultant MLS. In this paper, the principle of approximation by skewed arc magnets is proposed and discussed, and the performance of the MLS with skewed arc magnets is evaluated and compared with the ideal MLS in terms of thrust force, torque, and gear ratio. It is highlighted that the gear ratio is the fundamental indicator of the MLS’s performance, besides the thrust force and torque. It is shown that the gear ratio of MLS is mainly affected by the reference angle and the arc angle of skewed arc magnets, and optimal values for the two angles are obtained. In addition, the effects of the magnet magnetization on thrust force and torque are analyzed. Finally, dovetail budge and groove are introduced between the adjacent magnet segments for simpler fabrication. [ABSTRACT FROM AUTHOR]

Threaded fasteners are vastly used in the industry due to ease of mounting and dismounting and flexibility of design. Nonetheless, several researchers indicate that most failures recorded on nearly any kind of machinery and vehicles are initiated at fasteners. This review paper aims at summarising the failures involving threaded fasteners available in the literature. The analysis involves categorization of the failures according to the root cause, e.g., incorrect assembly, overload, fatigue, preload loss during operation, among others. The purpose of this review paper is to give a systematic glance of the topic to the scientific and technical community, to correctly orient future investigations on the broad topic of threaded fasteners. [ABSTRACT FROM AUTHOR]

This paper introduces a batch fabrication method to manufacture Micro-Electro-Mechanical System (MEMS) platforms for 3D integration of sensors spatially-distributed on a 2D plane. In the heart of the concept is a foldable MEMS structure with polymer hinges which is used to support and guide the assembly of the discrete planar sensors by means of folding them into a 3D shape, like origami, providing controlled distribution of sensors in space. Flexible hinges carrying the electrical interconnects are a critical structural element of the platform and material selection study for those is the main focus of this paper. We analyzed different materials for flexible hinges fabrication, including photo-definable polyimide and parylene-C. Three approaches for sensor integration are presented: 1) co-fabrication; 2) sensor drop-in; and 3) transfer bonding. The prototypes of structures with different flexible hinge materials were fabricated and evaluated based on their mechanical flexibility, chemical compatibility, and material outgassing. Parylene-C exhibited similar or better performances compared to polyimide, demonstrating in each of the experiments a higher degree of thermal flexibility up to 350 °C, a superior chemical resistance against hydrofluoric acid, and 2.9 times lower outgassing. [2020-0301] [ABSTRACT FROM AUTHOR]

This paper presents new rotational and longitudinal symmetric self-clinching fasteners to fabricate reliable connections in busbars with low electrical resistance for energy distribution systems. Connections consist of form-closed joints that are hidden inside regions where two busbars overlap. The investigation into the fabrication and performance of the new self-clinched joints involved finite element modelling and experimentation to determine the required forces and to evaluate the electric current flow and the electrical resistance at different service temperatures. The original design of the joints that was proposed in a previous work was modified to account for busbar strips of copper and/or aluminum with similar or dissimilar thicknesses, connected by means of self-clinching fasteners made from the same materials of the busbars, instead of steel. The effectiveness of the new self-clinched joints was compared to that of conventional bolted joints that are included in the paper for reference purposes. The results show that rotational symmetric self-clinching fasteners yield lighter fabrication and more compact joints with a similar electrical resistance to that of bolted joints. They also show that longitudinal symmetric self-clinching fasteners aimed at replicating the resistance-seam-welding contact conditions yield a reduction in electrical resistance to values close to that of ideal joints, consisting of two strips in perfect contact and without contaminant or oxide films along their overlapped surfaces. [ABSTRACT FROM AUTHOR]

As an important part of track inspection, the detection of rail fasteners is of great significance to improve the safety of train operation. Additionally, rail fastener detection belongs to small-target detection. The YOLOv4 algorithm is relatively fast in detection and has some advantages in small-target detection. Therefore, YOLOv4 is used for rail fastener status detection. However, YOLOv4 still suffers from the following two problems in rail fastener status detection. First, the features extracted by the original feature extraction network of YOLOv4 are relatively rough, which is not conducive to crack anomaly detection on rail fasteners. In addition, the traditional convolutional neural network has a larger number of parameters and calculations, which are difficult to run on the embedded system with low memory and processing power. To effectively solve those two problems, this paper proposes a rail fastener status detection algorithm based on MobileNet-YOLOv4 (M-YOLOv4). The edge features and texture features of rail fasteners are very important for rail fastener detection, and CSPDarknet53 cannot effectively extract the features of fasteners. The MobileNet is used to replace the CSPDarknet53 feature extraction network in the YOLOv4 algorithm, which can extract subtle features of rail fasteners and reduce the number of parameters and calculations of the algorithm. The experimental results show that the M-YOLOv4 algorithm has high detection accuracy and low resource consumption in rail fastener status detection. The false-alarm rate (FAR), missed-alarm rate (MAR), and error rate (ER) were 5.71%, 1.67%, and 4.24%, respectively, and the detection speed reached 59.8 fps. Compared with YOLOv4, the number of parameters and calculations were reduced by about 80.75% and 83.20%, respectively. [ABSTRACT FROM AUTHOR]

Hyperspectral imagery (HSI) has shown promising results in real-world applications. However, the technological evolution of optical sensors poses two main challenges in HSI classification: 1) the spectral band is usually redundant and noisy and 2) HSI with millions of pixels has become increasingly common in real-world applications. Motivated by the recent success of hybrid huberized support vector machines (HHSVMs), which inherit the benefits of both lasso and ridge regression, this paper first investigates the advantages of HHSVM for HSI applications. Unfortunately, the existing HHSVM solvers suffer from prohibitive computational costs on large-scale data sets. To solve this problem, this paper proposes simple and effective stochastic HHSVM algorithms for HSI classification. In the stochastic settings, we show that with a probability of at least $1-\varrho $ , our algorithms find an $\epsilon $ -accurate solution using $\tilde {O}({1}/{\lambda _{2}\epsilon })$ iterations. Since the convergence rate of our algorithms does not depend on the size of the training set, our algorithms are suitable for handling large-scale problems. We demonstrate the superiority of our algorithms by conducting experiments on large-scale binary and multiclass classification problems, comparing to the state-of-the-art HHSVM solvers. Finally, we apply our algorithms to real HSI classification and achieve promising results. [ABSTRACT FROM AUTHOR]

In power electronic applications, due to switching components, the inverter dc-bus generates current pulses at low frequencies and high frequencies (HFs). Usually, a dc-bus capacitor is sized to absorb the current peaks at the switching frequency and to stabilize the dc voltage. For decoupling the HF harmonics, an additional capacitor is required. This decoupling capacitor needs to have a low impedance over a wide range of frequencies. However, their performances for the HF range are highly limited by their stray inductance. In order to minimize the need for an additional decoupling capacitor at HFs, this paper proposes to improve the connections of the dc bus capacitor. Busbar configuration and printed circuit board allow reducing parasitics of the cabling. Several types of interconnections are proposed and characterized in this paper. Both two-terminal and four-terminal capacitors are evaluated and compared regarding operating frequency ranges. The measurement and modeling results show a significant improvement for HF decoupling by using quadrupole coaxial connections. [ABSTRACT FROM AUTHOR]

This paper deals with equivalent 2-D finite-element (FE) models of permanent magnet lead screw (PMLS), to accurately predict the thrust force. Due to the helical-shape permanent magnets (PMs), the magnetic fields of PMLS are typically 3-D and non-symmetric. However, the PMLS is approximately symmetric in some cases and an equivalent 2-D axis-symmetric FE model is developed instead of 3-D models. The problem lies in that no evaluation about the accuracy and applicability of the 2-D axis-symmetric FE model was offered in previous literature. In this paper, the 2-D axis-symmetric FE model is derived based on the concept of equivalent current sheets of PMs. The limitations of the existing 2-D axis-symmetric FE model are clarified, and a novel equivalent 2-D torque FE model is then proposed to predict thrust force with the gear ratio when the 2-D axis-symmetric FE model is not appropriate. It is shown that the choice of equivalent 2-D FE model depends on the lead angle of PMLS, 2-D axis-symmetric FE model is applicable in the range of (0°, 15°), and 2-D torque FE model is preferred in the range of (65°, 90°). [ABSTRACT FROM AUTHOR]

The fastening bolt is one of the important parts of the stacked structure proton exchange membrane electrolyzer (PEMEC), and the bolt tightening behavior has a significant impact on the electrochemical performance of the PEMEC. In this paper, we use a transparent visualization system to explore the effect of tightening bolt behavior on the performance of PEMEC. Pressure sensitive paper was used to record the contact pressure distribution map of PEMEC under different bolt tightening methods, and a high-speed camera was used to record the state of gas-liquid two-phase flow inside PEMEC under different bolt tightening methods. Both of them are used to verify the effect of bolt tightening behavior on the performance of PEMEC. The experimental results show that the contact pressure of PEMEC increases with the increase of bolt torque, and the electrochemical performance first increases and then decreases with the increase of bolt torque. The optimum bolt torque is 3.5 N m for a square PEMEC with an end plate size of 100 mm × 100 mm and a membrane size of 50 mm × 50 mm × 0.17 mm. The PEMEC performance increases with the increase of bolt tightening steps, and the optimal tightening step for the system is three steps. In addition, symmetrical tightening of bolts is beneficial for improving electrochemical performance, and there is an optimal tightening sequence in the system. This paper illustrates the relationship between tightened bolts and PEMEC electrochemical performance, showing the relationship between bolt tightening and the gas-liquid two-phase flow state inside PEMEC. The research results can provide a reference for optimizing the assembly and installation process of PEMEC. • A transparent visualization of PEMEC bolt tightening experimental method is proposed. • Minimum bolt torque for PEMEC installation is presented. • The optimal installation process for PEMEC is presented. [ABSTRACT FROM AUTHOR]

The rail fastener is an indispensable component used to connect the rail and sleepers in the track structure. Real‐time recognition of the fastener defects plays a vital role in ensuring the safe and stable operation of rail transit. In this paper, an intelligent and innovative method is proposed to detect the fastener defects including the invisible defects appearing as bolt loosening and the visible defects such as the worn or completely missing fasteners by using axle‐box vibration acceleration and deep learning network. First, the dynamical relation between the fastener defects and the axle‐box vibration acceleration is investigated by using the first principle and the vehicle–track dynamical model. Then a defects recognition network is built based on the deep convolution neural network for track fasteners by using the frequency spectrum images of the axle‐box vibration. The results show that the proposed method achieves a classification accuracy of 98.27%. Finally, the track section where the fasteners are most likely to be damaged is investigated, and rail corrugation is found to be a key factor that causes fastener fatigue. [ABSTRACT FROM AUTHOR]

With the remarkable recent progress on learning deep generative models, it becomes increasingly interesting to develop models for controllable image synthesis from reconfigurable structured inputs. This paper focuses on a recently emerged task, layout-to-image, whose goal is to learn generative models for synthesizing photo-realistic images from a spatial layout (i.e., object bounding boxes configured in an image lattice) and its style codes (i.e., structural and appearance variations encoded by latent vectors). This paper first proposes an intuitive paradigm for the task, layout-to-mask-to-image, which learns to unfold object masks in a weakly-supervised way based on an input layout and object style codes. The layout-to-mask component deeply interacts with layers in the generator network to bridge the gap between an input layout and synthesized images. Then, this paper presents a method built on Generative Adversarial Networks (GANs) for the proposed layout-to-mask-to-image synthesis with layout and style control at both image and object levels. The controllability is realized by a proposed novel Instance-Sensitive and Layout-Aware Normalization (ISLA-Norm) scheme. A layout semi-supervised version of the proposed method is further developed without sacrificing performance. In experiments, the proposed method is tested in the COCO-Stuff dataset and the Visual Genome dataset with state-of-the-art performance obtained. [ABSTRACT FROM AUTHOR]