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The lubricating fluid stored in the porous matrix will spontaneously exude to supplement the lubricating film in the damaged area, thus ensuring the long-term self-lubricating function of the porous surface. To reveal the repair mechanism of oil film, it is necessary to understand the flow characteristics of oil in micropores. The purpose of this study guides the design of micropore structure to realize the rapid exudation of oil to the porous surface and the rapid repair of the lubricating film.

In this paper, cylindrical orifice, convergent orifice and divergent orifice were studied. The numerical model of lubricating oil exudation in micropores was established. The distribution characteristics of oil pressure, velocity and three-phase contact line in the process of oil exudation were investigated. The effects of different orifice shapes and orifice structure parameters on the pinning and spreading characteristics of oil droplet were analyzed. Then the internal mechanisms of oil droplet formation and spread on the orifice surface were summarized.

The results show that during the process of oil exudation, the three-phase contact line of the oil drop is pinned once at the edge of the cylindrical and convergent orifice. Compared with the three orifice structures, the inlet pressure of the oil drop is low, and the oil velocity at the pinning point is stable in the divergent orifice. Resulting in favorable oil exudation. It is easier for oil droplet to depin by appropriately reducing the wall wetting angle, increasing the aperture or controlling the wall inclination angle. Ensure the self-healing and long-lasting lubrication film of porous oil-bearing surfaces.

The effect of pore structure on the flow behavior of lubricating fluid has always been concerned. But the mechanism by which different orifice shape affect the pinning behavior of oil droplets is not yet clear, which is crucial for understanding the self-healing mechanism of oil films on porous surfaces. It is meaningful to analyze the mechanism of oil exudation and spreading on the porous surface of oil in the special orifice, to optimize the design of the orifice structure.

Orifice shape has influence on internal flow field parameters. There is no report on the influence of orifice shape on the film formation process of oil seepage and diffusion from pores. The effects of different orifice shapes and orifice structure parameters on the characteristics of oil droplet pinning and diffusion were studied.

The peer review history for this article is available at: https://publons.com/publon/10.1108/ILT-04-2024-0118/

The occupational health risk associated with the production of prefabricated concrete components is often overlooked. This paper will use a damage assessment and cyclic mitigation (DACM) model to provide individualized exposure risk assessment and corresponding mitigation management measures for workers who are being exposed.

The DACM model is proposed based on the concept of life cycle assessment (LCA). The model uses Monte-Carlo simulation for uncertainty risk assessment, followed by quantitative damage assessment using disability-adjusted life year (DALY). Lastly, sensitivity analysis is used to identify the parameters with the greatest impact on health risks.

The results show that the dust concentration is centered around the mean, and the fitting results are close to normal distribution, so the mean value can be used to carry out the calculation of risk. However, calculations using the DACM model revealed that there are still some work areas at risk. DALY damage is most severe in concrete production area. Meanwhile, the inhalation rate (IR), exposure duration (ED), exposure frequency (EF) and average exposure time (AT) showed greater impacts based on the sensitivity analysis.

Based on the comparison, the DACM model can determine that the potential occupational health risk of prefabricated concrete component (PC) factory and the risk is less than that of on-site construction. It synthesizes field research and simulation to form the entire assessment process into a case-base system with the depth of the cycle, which allows the model to be continuously adjusted to reduce the occupational health damage caused by production pollution exposure.

This paper aims to provide a better understanding of the challenges and potential solutions in Visual Simultaneous Localization and Mapping (SLAM), laying the foundation for its applications in autonomous navigation, intelligent driving and other related domains.

In analyzing the latest research, the review presents representative achievements, including methods to enhance efficiency, robustness and accuracy. Additionally, the review provides insights into the future development direction of Visual SLAM, emphasizing the importance of improving system robustness when dealing with dynamic environments. The research methodology of this review involves a literature review and data set analysis, enabling a comprehensive understanding of the current status and prospects in the field of Visual SLAM.

This review aims to comprehensively evaluate the latest advances and challenges in the field of Visual SLAM. By collecting and analyzing relevant research papers and classic data sets, it reveals the current issues faced by Visual SLAM in complex environments and proposes potential solutions. The review begins by introducing the fundamental principles and application areas of Visual SLAM, followed by an in-depth discussion of the challenges encountered when dealing with dynamic objects and complex environments. To enhance the performance of SLAM algorithms, researchers have made progress by integrating different sensor modalities, improving feature extraction and incorporating deep learning techniques, driving advancements in the field.

To the best of the authors’ knowledge, the originality of this review lies in its in-depth analysis of current research hotspots and predictions for future development, providing valuable references for researchers in this field.

Automation of detecting cracked surfaces on buildings or in any industrially manufactured products is emerging nowadays. Detection of the cracked surface is a challenging task for inspectors. Image-based automatic inspection of cracks can be very effective when compared to human eye inspection. With the advancement in deep learning techniques, by utilizing these methods the authors can create automation of work in a particular sector of various industries.

In this study, an upgraded convolutional neural network-based crack detection method has been proposed. The dataset consists of 3,886 images which include cracked and non-cracked images. Further, these data have been split into training and validation data. To inspect the cracks more accurately, data augmentation was performed on the dataset, and regularization techniques have been utilized to reduce the overfitting problems. In this work, VGG19, Xception and Inception V3, along with Resnet50 V2 CNN architectures to train the data.

A comparison between the trained models has been performed and from the obtained results, Xception performs better than other algorithms with 99.54% test accuracy. The results show detecting cracked regions and firm non-cracked regions is very efficient by the Xception algorithm.

The proposed method can be way better back to an automatic inspection of cracks in buildings with different design patterns such as decorated historical monuments.