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The purpose of this paper is to use the corresponding magnetic sensor and detection method to detect and image the defects of small diameter pipelines. Urban gas pipeline is an energy transportation tool for urban industrial production and social life, which is closely related to urban safety. Preventing the occurrence of urban gas pipeline transportation accidents and carrying out pipeline defect detection are of great significance for the urban economic and social stability. To perform pipeline defect detection, the magnetic flux leakage internal detection method is generally used in the detection of large-diameter long-distance oil and gas pipelines. However, in terms of the internal detection of small-diameter pipelines, due to the heavy weight, large structure of the detection device and small pipe diameter, the detection is more difficult.

In order to solve the above matters, self-made three-dimensional magnetic sensor and three-dimensional magnetic flux leakage imaging direct method are proposed for studying the defect identification. Firstly, for adapting to the diameter range of small-diameter pipelines, and containing the complete information of the defect, a self-made three-dimensional magnetic sensor is made in this paper to improve the accuracy of magnetic flux leakage detection. And on the basis of it, a small diameter pipeline defect detection system is built. Secondly, as detection signal may be affected by background magnetic field interference and the jitter interference, the complete ensemble empirical mode decomposition with adaptive noise method is utilized to screen the detected signal. As a result, the useful signal is reconstructed and the interference signal is removed. Finally, the defect contour inversion imaging of detection is realized based on the direct method of three-dimensional magnetic flux leakage imaging, which includes three-dimensional magnetic flux leakage detection data and data segmentation recognition.

The three-dimensional magnetic flux leakage imaging experimental results shown that, compared to the actual defects, the typical defects, irregular defects and crack groove defects can be analyzed by the magnetic flux leakage defect contour imaging method in qualitative and quantitative way respectively, which provides a new idea for the research of defect recognition.

A three-dimensional magnetic sensor is made to adapt the diameter range of small diameter pipeline, and based on it, a small-diameter pipeline defect detection system is built to collect and display the magnetic flux leakage signal.

The major concern technologies during the processing through three-dimensional printing (3DP) are the mechanical and boundary properties of sand models. The parameters such as activator content, resolution X, layer thickness and re-coater speed play a vital role in 3DP sand components. The purpose of this paper is to recommend the optimal process parameters for the best sand mold properties.

In this paper, taking the parameters of the activator content, resolution X, layer thickness and re-coater speed as the influence factors, an orthogonal test of L16(44) was designed to discuss the influences of those parameters on the mechanical and boundary properties. Three-point bending (3PB) test was used to characterize the actual bending strength, and the boundary accuracy was assessed by the deviation of the three-point bending samples compared with its design scale.

The experimental results showed that the resolution X and layer thickness are the main parameters affecting sand mold properties. The strength will attain its maximum when the resolution X and layer thickness are the minimum. The optimal parameters were screened and verified by the confirmation test. The optimal process parameters for best strength and less gas evolution are the activator of 0.19%, resolution X of 0.1 mm, layer thickness of 0.28 mm and re-coater speed of 210 mm/s.

The novelty of this paper is the select of significant parameters on 3D-printed sand model properties. A mathematical model was built to analyze the effect of these parameters. The optimal process parameters for the best properties were got.

Technology addiction is an increasingly severe problem. TikTok has become increasingly popular recently, and its addiction is also a major concern. This study aims to examine the antecedents and outcomes of TikTok addiction.

The authors collect 579 data from Chinese users using an online survey. The authors use structural equation modeling with partial least squares (PLS-SEM) to analyze data and test hypotheses.

The results illustrate that perceived enjoyment, social relationship, utilitarian need and social influence positively affect TikTok addiction. Both social anxiety and loneliness have positive effects on TikTok addiction. Moreover, parasocial relationships positively moderate the association between the antecedents of self-determination theory (SDT) (perceived enjoyment, social relationship, utilitarian needs, social influence, social anxiety and loneliness) and TikTok addiction. Meanwhile, TikTok addiction intensifies conflicts, including technology-family conflict, technology-person conflict and technology-work conflict. These conflicts reduce life satisfaction.

It offers practical implications for preventing and avoiding TikTok addiction to create a healthy environment.

This study is one of the few to provide a complete process of TikTok addiction. It systematically investigates the antecedents and outcomes of TikTok addiction.