Simulation study of micro interface damage of particle reinforced metal matrix composites on vibration cutting

The finite element simulation of the micro interface of SiCp/Al composites under ultrasonic vibration cutting is described. The constitutive relationship of the matrix, SiC particles and interface is analyzed respectively, and a “matrix-interface-particle” dynamic physical simulation model is given. The cutting conditions of a single particle in three different cutting paths are simulated, and the removal mechanism and interface damage characteristics of SiC particles is analyzed. The reliability of the simulation results is analyzed by observing the SEM photos of the experimental samples

Design of a novel crack-free precipitation-strengthened nickel-based superalloy and composites for laser powder bed fusion

Avoiding cracking defects is crucial to ensuring processability in the laser powder bed fusion (LPBF) of metallic materials. In this study, a crack-free Ni-based superalloy with a high volume fraction of the γ′ phase was designed for the LPBF process using the thermodynamic approach. The results indicate that the designed SD01 Ni-based alloy was crack-free and over 21% of the spherical γ′ phase was uniformly distributed in the matrix after heat treatment. In addition, 1 wt.% TiB2 particles were introduced into the SD01 alloy to further enhance high-temperature mechanical performance. It was found that the morphology of the γ′ phase was altered from spherical to cubic structures, and its volume fraction increased from 21% to 40% after the TiB2 addition. The SD01-TiB2 composite exhibited an excellent combination of tensile strength (437.43 MPa) and elongation (7.71%) at 900 °C compared with the SD01 alloy (252.03 MPa, 3.02%). These findings provide a new metallic material design method for the LPBF of crack-free high-performance Ni-based materials

Passengers' likely behaviour based on demographic difference during an emergency evacuation in a Ro-Ro passenger ship

By examining the characteristics of passengers on a ship route between the Shandong and Liaodong Peninsula, through a questionnaire survey, this paper aims to address the likely behaviours of passengers during emergency evacuation and the demographic differences among these behaviours. A questionnaire survey of 1380 passengers shows that passengers on board are more alert and are more likely to proactively respond to evacuation alarms (62.5%), observe others’ actions (59.1%), follow evacuation instructions (67.9%), obey the crew (66.2%), queue patiently (63%), return to the cabin when their families are left behind (65.1%), and be cooperative (59%) rather than competitive (44%). The multinomial logistic regression results show that passengers who are older, with limited mobility, that have more experience aboard ships and are part of a larger group, will be more likely to proactively confirm the authenticity of evacuation events. Men, elderly individuals, people who are part of a larger group and with less experience in evacuation education are more likely to follow others. When the family is left behind, elderly individuals and people who are part of a larger group are much more likely to choose to return to their cabins. Similarly, elderly passengers with larger groups are much more likely to choose to help others. Although questionnaire research has some limitations, such as a hypothetical response and closed questions, the research results are of great significance for helping passenger ship managers to develop appropriate management rules, and conduct effective evacuation education activities

User interest based dynamic VRML downloading for 3D shopping mall

As the Internet advances rapidly and becomes part of our daily life, web-based electronic commerce is getting popular and begins to take roots. However, a typical presentation of the static 2D text and image information does not attract users as much as real shops do because it gives users a very limited opportunity to manipulate or observe products. To allow e- commerce users to experience as close as possible to real life shopping, the 3D virtual shopping mall is introduced.Master of Science (Communication Software and Networks

Evolutions of CO2 Adsorption and Nanopore Development Characteristics during Coal Structure Deformation

The coal structure deformation attributed to actions of tectonic stresses can change characteristics of nanopore structure of coals, affecting their CO2 adsorption. Three tectonically deformed coals and one undeformed coal were chosen as the research objects. The isotherm adsorption experiments of four coal specimens were carried out at the temperature of 35 °C and the pressure of 0 to 7 MPa. Nanopore structures were characterized using the liquid nitrogen adsorption method. The results show that there exist maximum values of excess and absolute adsorption capacity, which increase with increasing coal deformation degree. As the degree of coal deformation increases, the pore volume and specific surface area present an obvious increasing trend in the case of micropores, exhibiting an increase at first (cataclastic coal and ganulitic coal) and then stabilization (crumple coal), in the case of mesopores, and showing a gradual decrease in the case of macropores. The mesopores are the key factor of CO2 adsorption of tectonically deformed coals, followed by the micropores and the limited effect of macropores at the strong coal deformation stage

Ship Type Recognition Based on Ship Navigating Trajectory and Convolutional Neural Network

With the aim to solve the problem of missing or tampering of ship type information in AIS information, in this paper, a novel ship type recognition scheme based on ship navigating trajectory and convolutional neural network (CNN) is proposed. Firstly, according to speed and acceleration of the ship, three ship navigating situations, i.e., static, normal navigation and maneuvering, are integrated into the process of trajectory images generation in the form of pixels. Then, three kinds of modular network structures with different depths are trained and optimized to determine the appropriate convolutional neural network structure. In the validation phase of the model, a large amount of verified data with a time span of one month was used, covering a variety of water conditions including open water, ports, rivers and lakes. Following this approach, a kind of CNN scheme which can be directly used to identify ship types in a wide range of waters is proposed. This scheme can be used to judge the ship type when the static information is completely missing and to test the data when the ship type information is partially missing

Ship Type Recognition Based on Ship Navigating Trajectory and Convolutional Neural Network

With the aim to solve the problem of missing or tampering of ship type information in AIS information, in this paper, a novel ship type recognition scheme based on ship navigating trajectory and convolutional neural network (CNN) is proposed. Firstly, according to speed and acceleration of the ship, three ship navigating situations, i.e., static, normal navigation and maneuvering, are integrated into the process of trajectory images generation in the form of pixels. Then, three kinds of modular network structures with different depths are trained and optimized to determine the appropriate convolutional neural network structure. In the validation phase of the model, a large amount of verified data with a time span of one month was used, covering a variety of water conditions including open water, ports, rivers and lakes. Following this approach, a kind of CNN scheme which can be directly used to identify ship types in a wide range of waters is proposed. This scheme can be used to judge the ship type when the static information is completely missing and to test the data when the ship type information is partially missing