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This study employed large eddy simulation and a volume-of-fluid with discrete phase model to evaluate a swirl fuel injector's atomization performance. Addressing non-uniform atomization at low flow rates, the study optimized a swirl injector structure based on additive manufacturing advantages during re-modeling. Computational analysis revealed that vortex downstream and capillary bubbles caused non-uniformity in the prototype injector, mitigated by the optimized injector's three-dimensional flow channel. Comparative analysis showed similar parameters between the optimized and prototype injectors, except for significant improvement in circumferential uniformity at low flow rates (from 41.48% to 14.69%). The optimized injector's swirl structure, produced via micro laser powder bed fusion, exhibited precise dimensions and minimal surface roughness. Validation experiments without air inflow confirmed the computational results' reliability, with a minor discrepancy in circumferential uniformity (2.98%) and atomization cone angle (2.3°) for the prototype swirl structure. At low flow rates, the optimized structure showcased reduced circumferential non-uniformity (from 42.31% to 28.76%), underscoring its benefits. This interdisciplinary investigation underscores additive manufacturing's application in structural optimization and manufacturing. [ABSTRACT FROM AUTHOR]

Defect detection on steel surfaces with complex textures is a critical and challenging task in the industry. The limited number of defect samples and the complexity of the annotation process pose significant challenges. Moreover, performing defect segmentation based on accurate identification further increases the task's difficulty. To address this issue, we propose VQGNet, an unsupervised algorithm that can precisely recognize and segment defects simultaneously. A feature fusion method based on aggregated attention and a classification-aided module is proposed to segment defects by integrating different features in the original images and the anomaly maps, which direct the attention to the anomalous information instead of the irregular complex texture. The anomaly maps are generated more confidently using strategies for multi-scale feature fusion and neighbor feature aggregation. Moreover, an anomaly generation method suitable for grayscale images is introduced to facilitate the model's learning on the anomalous samples. The refined anomaly maps and fused features are both input into the classification-aided module for the final classification and segmentation. VQGNet achieves state-of-the-art (SOTA) performance on the industrial steel dataset, with an I-AUROC of 99.6%, I-F1 of 98.8%, P-AUROC of 97.0%, and P-F1 of 80.3%. Additionally, ViT-Query demonstrates robust generalization capabilities in generating anomaly maps based on the Kolektor Surface-Defect Dataset 2. [ABSTRACT FROM AUTHOR]

This study aims at investigating the intensity of environmental regulation on firm's share of domestic value-added in gross exports in China. An unbalanced firm-level panel data from 2000 to 2014 is adopted to estimate 10 two-way fixed effects models suggested by Hausman test for the entire sample of 648,801 and subsamples of three trade patterns, three types of ownership and three regions. The primary finding of this study is that for the entire sample and subsamples of general trade, mixed trade, non-state-owned and foreign-owned enterprises and enterprises in the eastern region, the intensity of environmental regulation has a "U-shaped" influence on firm's share of domestic value-added in gross exports in China, meaning that this influence is negative first and then turns to positive after the intensity of environmental regulation reaches a certain level. This conclusion is reasonable and consistent with our expectation. [ABSTRACT FROM AUTHOR]

Parts with a large depth-to-diameter ratio play a critical role in the military, aerospace, and automotive industries. However, accurately measuring their inner surface profile remains challenging owing to the lack of adequate and accurate sensors. We developed a multi-ring structured light system to obtain three-dimensional data of inner contours, such as inner diameters, which are crucial for ensuring component performance and safety. In this study, we proposed three simple yet effective techniques to improve the multi-ring beam structured measurement system. First, we designed a distortion correction method to calibrate the imaging system. Second, a two-step calibration approach was used to calibrate the multi-ring projection. Meanwhile, we benchmarked the effects of different light sources on image speckles. The calibration results demonstrated that the coefficient of determination (R-2) used for line fitting exceeded 0.999. Moreover, the measurement experimental results show that the uncertainty of less than 10 µm and the smallest measurable pipe inner diameter can reach 15 mm, demonstrating that our methods are promising for improving the accuracy of structured light optical sensing systems. This system satisfies the measurement requirements and can be immediately utilized to meet the high demand for inner contour measurements in industrial applications. [ABSTRACT FROM AUTHOR]

This paper focuses on discerning the melting state of keyhole tungsten inert gas (K-TIG) welding, a promising welding technique still considered a novelty. In order to achieve this goal, a sensing system is established to collect voltage signals during the welding process of 304 stainless steel. Analysis of the collected signals reveals that the characteristic signal power spectrum between 36 kHz and 37 kHz is closely related to whether the welding has complete joint penetration. The experimental findings confirm that the spectral power density of the characteristic signal varies significantly with the alteration of welding quality. Moreover, it is observed that when the welding melting state remains unchanged, the change in welding parameters is not noteworthy. When adjusting welding parameters to maintain stable feature signals during the welding process, the corresponding penetration state of the welding does change. Therefore, it is possible to ensure the stability of welding quality by altering welding parameters in real time. These research results provide valuable insights into the real-time quality monitoring of the K-TIG welding process. [ABSTRACT FROM AUTHOR]

Surface-mounted high-speed permanent magnet motors are known for their small size, high power factor, and reliable control and operation. However, these motors may have low mechanical strength and weak tensile strength in their rotor magnet, which necessitates the adoption of an external sleeve for protection. Currently, carbon fiber composite sleeves are garnering increasing attention for their durable and reliable protective capabilities. Despite the apparent benefits of carbon fiber composite protective sleeves, systematic research on the relevant parameters such as winding angle, fiber type, resin type, winding tension, and curing method is currently lacking in the literature for high-speed permanent magnet motors. In this context, this paper employs an innovative approach, utilizing the Taguchi method, the National Ordnance Laboratories (NOL) ring failure test, and a resin content test, to investigate the winding process parameters of carbon fiber composite protective sleeves for high-speed permanent magnet motors. The results indicate that the tensile strength of the composite sleeve with a winding angle of 45 and 30° is reduced by 20~25% compared with the sleeve with a pure hoop winding angle. The loss of strength caused by the manufacturing process accounts for 30%. When the wet winding tension is 130 N, the tensile and shear strength of the sleeve decreases by 13 and 12%, respectively, compared with that of 70 N. When the dry winding temperature rise rate is 3 min/°C, the strength of the sleeve decreases by 16% compared with that of 1 min/°C. For a small-thickness sleeve with a thickness of 1.5 mm, two-layer curing can increase the tensile strength of the sleeve, while three-layer curing or more can decrease the strength. By studying the process parameters of the sleeve, this article prepared a composite sleeve for a high-speed permanent magnet motor with a line speed of 200 m/s. This study provides a comprehensive analysis of the manufacturing process parameters of the rotor sleeve, which is helpful for the design and performance optimization of high-speed permanent magnet motors. [ABSTRACT FROM AUTHOR]

Selecting the single best blastocyst based on morphological appearance for implantation is a crucial part of in vitro fertilization (IVF). Various deep learning and computer vision-based methods have recently been applied for assessing blastocyst quality. However, to the best of our knowledge, most previous works utilize classification networks to give a qualitative evaluation. It would be challenging to rank blastocyst quality with the same qualitative result. Thus, this paper proposes a regression network combined with a soft attention mechanism for quantitatively evaluating blastocyst quality. The network outputs a continuous score to represent blastocyst quality precisely rather than some categories. As to the soft attention mechanism, the attention module in the network outputs an activation map (attention map) localizing the regions of interest (ROI, i.e., inner cell mass (ICM)) of microscopic blastocyst images. The generated activation map guides the entire network to predict ICM quality more accurately. The experimental results demonstrate that the proposed method is superior to traditional classification-based networks. Moreover, the visualized activation map makes the proposed network decision more reliable.