19684 research outputs found
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Pressure-less joining materials for SiC-based components for light water reactors
No full textSilicon carbide fiber-reinforced composites (SiC/SiC) are leading candidates to replace zirconium-based alloys as cladding in light water reactors (LWR), owing to their exceptional oxidation resistance and mechanical performance under accident conditions. However, pressure-less joining methods compatible with the extreme chemical and thermal environment of LWRs remain a major technological hurdle. This work evaluates two promising joining materials—Mo-wrap (a MoSi₂/Si composite) and SAY (a silica–alumina–yttria glass-ceramic)—under simulated LWR conditions. Joining was performed using both conventional furnaces and laser-assisted techniques. Joint integrity and microstructure were assessed by SEM/EDS and X-ray computed tomography. Hydrothermal stability was evaluated in static and flowing-water (loop) autoclaves up to 30 days at 330 °C and 150–155 bar. Mo-wrap joints showed partial degradation due to silicon dissolution, while SAY joints retained good structural integrity in static tests but suffered phase-selective corrosion under flowing conditions, with keivite emerging as the most stable crystalline phase. Laser-processed amorphous SAY joints exhibited improved corrosion resistance, though still limited under prolonged exposure. These findings advance the understanding of joining performance in nuclear-relevant environments and support the development of accident-tolerant fuel cladding.</p
BPDF-SegNet:a bidirectional perception and dynamic fusion segmentation network to detect ball screw small pitting defects
No full textIt is a challenging task to detect small damages on ball screw surface by using detection using computer vision techniques due to low contrast backgrounds and significant texture interferences. Current segmentation techniques based on U-Net uses the traditional feature fusion mechanism in the decoder, which makes it challenging to reliably extract sub-pixel-level edge details, hence leading to edge blurring or local breaks in the segmentation mask. Due to loss of detail information, it causes the issue of missed and false detection. This research proposes bidirectional perception and dynamic fusion segmentation network as a solution to this issue. Firstly, a bidirectional decoupled region calibration module is introduced to improve the model’s capability to focus on foreground targets. Secondly, the feature maps are effectively up-sampled using deep convolution and channel shift techniques to make over for any lost features. Thirdly, the feature fusion process facilitates the effective fusing of the feature maps, enabling the network to better capture complementary information across different layers. Lastly, the focal loss function and Tversky loss function are integrated during the model training stage to reduce the impact of sample imbalance. Experiments conducted on the ball screw dataset demonstrate that the approach produces improved segmentation outcomes and offers a fresh solution for detecting industrial metal surface flaws.</p
A metamaterial beam with multi-piezoelectric patches (MB-MPP) for weak vibration enhancement in rotating machinery fault diagnostics
No full textCondition monitoring of rotating machinery constitutes essential approaches for enhancing the reliability and safety of mechanical systems. Vibration signal sensing technology has emerged as a pivotal tool in rotating machinery fault diagnosis, offering advantages of rich information content and accessibility. However, conventional fault diagnosis approaches heavily depend on high-performance sensors and advanced signal processing to determine optimal resonance bands for high signal-to-noise ratio (SNR) fault characteristics, which suffers from high cost and intricate algorithm deployments. To address these limitations, this paper proposed a new vibration sensing method based on a gradient metamaterial beam, which is shorten as metamaterial beam with multi-piezoelectric patches (MB-MPP) for brevity. The MB-MPP structure is designed to have multiple frequency bands for weak fault signal enhancement, and multiple piezoelectric patches are integrated into MB-MPP to convert the dynamic stress into voltage signals, achieving a multi-band sensing system. The design and optimization of MB-MPP for desired frequency bands was first carried out based on the mechanism of the rainbow trapping. Subsequently, the frequency band characteristics and the signal enhancement were tuned and validated by finite element simulations so that MB-MPP can achieve multiple discrete frequency bands which meet the need for monitoring commonly used machines. Finally, experiment evaluations were conducted based on the rotating machinery to prove the performance of the designed metamaterial. The results show that the SNRs of fault signals can be improved by over 50% in diagnosing common faults in rolling bearings and gears, which ensures the accuracy and reliability of diagnostics significantly
Fault diagnosis method based on multimodal-deep tensor projection network under variable working conditions
No full textIn the traditional fault diagnosis method based on convolutional neural network, the dimension of the higher-order input tensor of the pooling layer is reduced under variable working conditions, the tensor data are easily destroyed to cause the loss of data information. In addition, the diagnosis of single modal features will ignore the coupling of fault information under variable working conditions and lack the joint extraction of other modes, so that the model performance is restricted. To overcome these deficiencies, combining the advantages of the tensor projection layer and multimodality, a new fault diagnosis method based on a multimodal-deep tensor projection network is proposed under variable working conditions. In the proposed method, the multimodal features obtained by modulating and demodulating vibration signals are transformed into a time–frequency map, and the obtained time–frequency maps are fused to construct a third-order tensor composed of time, frequency, and modal number. Then a multimodal-deep tensor projection network is constructed by tensor projection layers instead of pooling layers in traditional deep convolution neural networks. The proposed method avoids the destruction of higher-order input tensor dimension reduction and the loss of information. The recognition accuracy has greatly improved. The proposed method is verified by the bearing fault diagnosis experiments of speed-up and speed-down processes under variable working conditions, and the inter-shaft bearing fault dataset from an aero-engine system. The experimental results show that the proposed method is very effective. The proposed method contains more dimensional feature information, can better extract fault features and improve the recognition rate of different types of faults
Optimizing robotic collection point for accurate mechanical anomaly noise source localization within an indoor sound field environment
No full textTraditional sound source localization faces significant challenges when encountering complex sound fields in industrial environments. However, integrating it into robots offers numerous advantages for monitoring large-scale mechanical equipment. The intricate characteristics of indoor sound fields (ISF) and the sound radiation mechanisms of motors have been thoroughly analyzed by employing modelling and acoustics simulations. Additionally, the acoustic signal qualities at various positions are comprehensively compared and evaluated. To enhance the accuracy of abnormal noise source localization, a novel method of optimal collection point (OCP) of the robot based on the comprehensive feature difference ratio of the multiple metrics has been proposed. The optimal signal quality positions have been precisely identified through this approach. Finally, the superiority and applicability of abnormal noise source localization based on the OCP are validated through experimental tests. The localization error of the abnormal noise source localization under different speed conditions is within 1%, and the localization error under different load conditions with interference can still be within 3%. These provide a new perspective for the localization of mechanical anomaly within complex ISF and facilitate more efficient and reliable equipment monitoring in industrial environments.</p
An overview of sound source localization based condition monitoring robots
No full textAs artificial intelligence advances and demand for cost-effective equipment maintenance in various fields increases, it is worth insightful research on utilizing robots embedded with sound source localization (SSL) technology for condition monitoring. Combining the two techniques has significant advantages, which are conducive to further classifying and tracking abnormal sources, thereby enhancing system performance at a lower cost. The paper provides an overview of current acoustic-based robotic techniques for condition monitoring, highlights the common SSL methods, and finds that localization performance heavily depends on signal quality. The advantages of combining robotics with SSL are then summarized, especially the emergence of non-synchronous measurement, which is more suited for integrating acoustic monitoring with mobile robots. Therefore, we proposed the framework of SSL based condition monitoring robots and discussed their application prospects in various fields. Finally, several challenges in this respect and new research perspectives for future studies are summarized.</p
Transformative value in cultural tourism:Scale development and its impact on tourist eudaimonic well-being
No full textCultural tourism has the potential to foster significant tourist transformation, yet the concept of transformative value in cultural tourism and its relationship with tourist eudaimonic well-being remain underexplored. Through in-depth interviews with 29 tourists who experienced transformation after a cultural tourism experience, this study clarified the key components of transformative value. Following a rigorous scale development process, this study adopted a survey methodology to develop a reliable measurement scale for transformative value. Applying structural equation modeling, the study also investigated how transformative value affects tourist eudaimonic well- being. The findings revealed that transformative value in cultural tourism is a multidimensional construct, comprising personal self-enhancement, transpersonal self- expansiveness, cultural identification, and cultural knowledge acquisition. Self- reflection promoted transformative value. With the exception of transpersonal self- expansiveness, these dimensions significantly impacted upon eudaimonic well-being. The findings underscore the significance of creating transformative value in cultural tourism. This study also provides a robust tool for the measurement of tourists perceived transformative value, which can be used to explore antecedents and consequences. Practical insights for destination managers and service providers are also offered. By applying the scale, managers can assess and enhance offerings to enhance transformative values such as personal growth and cultural immersion, ultimately fostering eudaimonic well-being
Internal governance challenges of young independent coffee cooperatives in South-west Ethiopia
No full textThis study analyses the internal governance practices of coffee marketing cooperatives in south-west Ethiopia. The investigation argues that there is a need to go beyond democratic governance practices in order to understand the governance of these cooperatives working in multi-stakeholder environments where the state is powerful and intervenes in their operations. The study concludes that the actions of internal and external actors have resulted in unique governance structures that profoundly influence their operations
Use of the theory of critical distances in predicting medium cycle fatigue failure in five-axis ball end milled components
No full textWhile casting may have historically been used to make components with free-form surfaces, five-axis milling with ball end cutters is becoming increasingly common. Although this form of production enables the use of more robust billet material rather than cast material, it generates identifiable machining cusps on the component’s surface. A detailed FEA was conducted on CAD modelled specimens with detail of cusps defined allowing the extraction of peak stress values and stress fields generated by distinct geometry of cusps. Two strategies, the TCD-PM and TCD-LM methods, were implemented to predict life of five-axis machined specimens. Results showed that the TCD-LM method tends to be more accurate than the TCD-PM method. The TCD-LM method showed acceptable results for longer life specimens, however 40 % of data was still seen to fall outside acceptable ±2 scatter bands. TCD conservative results are judged to be the drawback of not considering the 3D stress raiser and plasticity effects
Preanalytical variables and analytes in liquid biopsy approach for brain tumors:A comprehensive review and recommendations from the RANO Group and the Brain Liquid Biopsy Consortium
No full textThis review explores the pivotal role of preanalytical variables in bringing liquid biopsy approaches into the clinic for brain tumors. Preanalytical variables encompass a range of critical issues, from blood sample collection and handling to the impact of tumor heterogeneity and patient-specific factors. These variables introduce challenges such as false positives, false negatives, and variability in the analysis of tumor signals, which can hinder the diagnostic and prognostic utility of liquid biopsies. Understanding the nuances of preanalytical variables is essential for the successful implementation of liquid biopsy in clinical settings. This paper delves into strategies aimed at mitigating the influence of preanalytical variables by emphasizing the importance of standardized sample collection protocols, optimized sample processing and storage, quality control measures, and the integration of multiple liquid biopsy modalities.</p
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