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Because of the advantages of high deposition efficiency and low manufacturing cost compared with other additive technologies, robotic wire arc additive manufacturing (WAAM) technology has been widely applied for fabricating medium- to large-scale metallic components. The additive manufacturing (AM) method is a relatively complex process, which involves the workpiece modeling, conversion of the model file, slicing, path planning and so on. Then the structure is formed by the accumulated weld bead. However, the poor forming accuracy of WAAM usually leads to severe dimensional deviation between the as-built and the predesigned structures. This paper aims to propose a visual sensing technology and deep learning–assisted WAAM method for fabricating metallic structure, to simplify the complex WAAM process and improve the forming accuracy.

Instead of slicing of the workpiece modeling and generating all the welding torch paths in advance of the fabricating process, this method is carried out by adding the feature point regression branch into the Yolov5 algorithm, to detect the feature point from the images of the as-built structure. The coordinates of the feature points of each deposition layer can be calculated automatically. Then the welding torch trajectory for the next deposition layer is generated based on the position of feature point.

The mean average precision score of modified YOLOv5 detector is 99.5%. Two types of overhanging structures have been fabricated by the proposed method. The center contour error between the actual and theoretical is 0.56 and 0.27 mm in width direction, and 0.43 and 0.23 mm in height direction, respectively.

The fabrication of circular overhanging structures without using the complicate slicing strategy, turning table or other extra support verified the possibility of the robotic WAAM system with deep learning technology.

The purpose of this study is to investigate the influence of dimple radius, depth and density on the lubrication performance of the plunger.

A lubrication model was adopted to consider eccentricity and deformation during the working process of the plunger, and a rig test was performed to confirm the simulation results. The texture was fabricated using laser surface texturing.

The simulation results suggested that when dimple radius or depth increases, oil film thickness of the plunger increases before decreasing, and asperity friction displays an opposite trend. Therefore, appropriate microdimple texture could facilitate lubrication performance improvement and reduce the wear. Microdimples were then lased on the plunger surface, and a basic tribological test was conducted to validate the simulation results. The experimental results suggested that the average friction coefficient decreased from 0.18 to 0.13, a reduction of 27.8%.

The introduction of microdimple on a plunger couple to reduce friction and improve lubrication is expected to provide a new approach to developing high-performance plunger couple and improve the performance of the internal combustion engine. If applied, the surface texture could help reduce friction by around 27% and cap the cost relative to the plugger friction.

The microdimple texture was introduced into the plunger couple of a vehicle to reduce the friction and improve the performance. Findings suggested that surface texture could be used in the automotive industry to improve oil efficiency and lubrication performance.

The peer review history for this article is available at: http://dx.doi.org/10.1108/ILT-07-2020-0259.

The application of massive open online courses (MOOCs) helps integrate sustainable development goals (SDGs) into architectural curricula. The essence of MOOC development is building an education platform that promotes the sharing and continuing of global education resources.

This study establishes a four-dimensional evaluation model based on the four characteristics of MOOCs. The quadrilateral area evaluation method is used to create an evaluation radar chart to comprehensively evaluate satisfaction and demand in the traditional teaching model of architectural technology. This study discusses whether the curriculum is suitable for the development of MOOCs and how to optimize the sustainable pedagogical mode according to its characteristics to meet future teaching needs and realize the sustainable development of education.

Satisfaction evaluation found that current education is not open enough from the students' perspective; therefore, MOOCs enhance students' participation and significantly reduce future learning costs. Through demand evaluation, it was found that both teachers and students believed that the lack of direct and effective communication between them and the difficulty in ensuring the learning effect were problems that must be addressed in MOOCs.

This study focused on the sustainability of MOOCs in curriculum development. It emphasizes the combination of MOOCs' teaching modes and the course itself and provides specific guidance and suggestions for improving the course. It uses an evaluation method for objective analysis and visualization.

The purpose is to provide decision support for tourists recommending scenic spots and corresponding suggestions for the management of scenic spots.

Based on the Baidu index data generated, this paper analyzes the temporal and spatial characteristics of network attention of 5A scenic spots in Sichuan Province. The online comment data are used to build the assessment model of scenic spots based on network attention, and the comment information of tourists is mined and analyzed through statistical analysis. At the same time, the key attributes of scenic spots from the perspective of network attention are evaluated and analyzed by using the probabilistic linguistic term set. Finally, this paper further constructs a recommendation model based on the key attribute set of scenic spots.

This paper uses different types of tourism network information, integrates multi-types of data and methods, fully excavates the value information of tourism network information, constructs the research framework of “scenic spot assessment + scenic spot recommendation” from the perspective of network attention, analyzes the network attention characteristics of scenic spots, evaluates the performance of scenic spots, and implements scenic spot recommendation.

This paper integrates multi-source data and multidisciplinary theoretical methods to form a scenic spot research framework of “assessment + recommendation” from the perspective of network attention.