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Over the past few decades, there have been many important achievements on the design, research, and development of minimal surface lattice structures. In this work, we propose a modeling method for triply periodic minimal surfaces (TPMS) lattice structures with complex contours. This method is based on moving elements method (MEM) and mainly includes the following parts: dividing the model mesh, solving the iso-surface of the TPMS in the model, obtaining the TPMS lattice structure triangular surface of the model outline, integrating the interior of the model and the outline, and finally generating an STL file that can be used for additive manufacturing. Furthermore, the representative femur model, rabbit model, and gear model are provided as case studies to verify the validity and correctness of the proposed modeling method. Then, this approach is compared with the distance field-based method (DFBM) in terms of modeling speed and modeling accuracy. The research results show that the time required by the MEM method was reduced by 3055.56%, 2799.70% compared to the DFBM method, when filling the network primitive and network gyroid lattice structures in the femur model. Moreover, this method provides technical means and simulation data for designing TPMS lattice structures with complex contour, and offers the underlying design ideas for the development and application of the excellent physical properties of TPMS lattice structures in medicine and engineering. [ABSTRACT FROM AUTHOR]

The lattice structure based on minimal surface (also called the triply periodic minimal surfaces) is characterized by zero-value average curvature and high circulation, which leads to better mechanical performance than the CAD (computer aided design)-based structures. In this paper, the principle of least action was introduced to explain the extreme value phenomenon of minimal surface area, zero means curvature of the minimal surface, and the excellent mechanical properties. Firstly, a series of lattice structures with different configurations and the same volume fraction were fabricated by selective laser melting (SLM) with TC4 powder through changing the design method, including CAD design and minimal surface. Secondly, the solidworks software and the quasit-static compression are used to investigate the surface area of experimental samples, surface curvature, and compression responses. The results show that the zero-value mean curvature of the minimal surface follows the principle of least action. Also shown is that the minimal surface-based lattice structure has better mechanical properties than the CAD design method, which is attributed to the minimal surface that makes the nodes of the lattice structure smooth transition. The proposed concept is demonstrated through the comparison of the mechanical properties of representative specimens. The approach, which is generalized to the optimal design of lattice structures, provides a way toward functional gradient structures that need to simultaneously carry a load and achieve efficient heat dissipation or biocompatibility, such as load-bearing exchangers or bone implantation. [ABSTRACT FROM AUTHOR]

Understanding the dynamic characteristics of surrounding vehicles and estimating the potential risk of mixed traffic can help reliable autonomous driving. However, the existing risk assessment methods are challenging to detect dangerous situations in advance and tackle the uncertainty of mixed traffic. In this paper, we propose a probabilistic driving risk assessment framework based on intention identification and risk assessment of surrounding vehicles. Firstly, we set up an intention identification model (IIM) via long short-term memory (LSTM) networks to identify the intention possibility of the surrounding vehicles. Then a risk assessment model (RAM) based on the driving safety field is employed to output the potential risk. Specifically, driving safety field can reflect the coupling relationship of drivers, vehicles, and roads by analyzing their interaction. Finally, an integrated risk evaluation model combining both IIM and RAM is developed to form a dynamic potential risk map considering multi-vehicle interaction. For example, in a typical but challenging lane-changing scenario, an intelligent vehicle can assess its driving status by calculating a risk map in real time that represents the risk generated by the estimated intentions of surrounding vehicles. Furthermore, simulations and naturalistic driving experiments are conducted in the extracted lane-changing scenarios, and the results verify the effectiveness of the proposed model considering lane-changing behavior interaction. [ABSTRACT FROM AUTHOR]