In complex discrete manufacturing environment, there used to be a poor network and an isolated information island in production line, which led to slow information feedback and low utilization ratio, hindering the construction of enterprise intelligence. To solve these problems, uncertain factors in the production process and demands of sensor network were analyzed; hierarchical topology design method and the deployment strategy of the complexity industrial internet of things were proposed; and a big data analysis model and a system security protection system based on the network were established. The weight of each evaluation index was calculated using analytic hierarchy process, which established the intelligentized evaluation system and model. An actual production scene was also selected to validate the feasibility of the method. A diesel engine production workshop and the enterprise MES were used as an example to establish a network topology. The intelligence level based on both subjective and objective factors were evaluated and analyzed considering both quantitative and qualitative aspects. Analysis results show that the network topology design method and the intelligentize evaluation system were feasible, could improve the intelligence level effectively, and the network framework was expansible.
Fast nondominated sorting genetic algorithm II (NSGA-II) is a classical method for multiobjective optimization problems and has exhibited outstanding performance in many practical engineering problems. However, the tournament selection strategy used for the reproduction in NSGA-II may generate a large amount of repetitive individuals, resulting in the decrease of population diversity. To alleviate this issue, Lévy distribution, which is famous for excellent search ability in the cuckoo search algorithm, is incorporated into NSGA-II. To verify the proposed algorithm, this paper employs three different test sets, including ZDT, DTLZ, and MaF test suits. Experimental results demonstrate that the proposed algorithm is more promising compared with the state-of-the-art algorithms. Parameter sensitivity analysis further confirms the robustness of the proposed algorithm. In addition, a two-objective network topology optimization model is then used to further verify the proposed algorithm. The practical comparison results demonstrate that the proposed algorithm is more effective in dealing with practical engineering optimization problems.
Abstract.Bearings are widely used in aerospace and other fields, its performance directly affects the production efficiency and safety. Nowadays, virtual simulation technology has become an indispensable part of intelligent manufacturing field. As a virtual simulation technology, FEA has been widely used in bearing design. China needs to import many aerospace bearings every year in aerospace area, Chinese national defense and other high precision technology is limited because the blockade of advanced bearing technology. We can use dynamics modeling and virtual simulation technology to achieve the predictive design, and strive to achieve foreign level. In this paper, the author proposed a method of bearing design based on virtual simulation technology. The factors of bearing which affect the dynamic characteristics are considered, the process of design bearing based on virtual simulation is also considered. According to the different design parameters, the simulation results are used to verify the rationality, these can reduce the cost and improve the reliability. The virtual simulation technology is applied to design the 7016C angular contact ball bearing which used in aerospace area, and supported decision-making in structure design and data analyze. Finally, The feasibility of this method is verified by experiments..
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