Curing process has a great impact on molding quality of the fiber-winding composite shell. In order to improve its curing quality and efficiency, a new curing process employed the method of heating the internal metal mandrel with steam can be adopted, which is called internal curing process. This paper introduces the principle of internal curing process and establishes the mathematical models of internal curing process, adopts ANSYS and APDL to develop the 3-D transient numerical simulation program of internal curing to realize numerical simulation of temperature, curing degree and residual strain, and analyze the influence of shell thick, fiber volume fraction and film coefficient on the simulation results. Taking the cylinder shell as an example, numerical simulation and experiment are carried out. This research provides theory basis and analysis method for the design, simulation and parameter optimization of internal curing process.
In the curing process of thermosetting prepreg compression molding (PCM), the distribution of the temperature field and the curing degree field have an important influence on the performance of composites. Therefore, the establishment of method to accurately predict the temperature difference and the degree of cure (DoC) difference during the curing process is significance for improving the performance of composites. In this paper, three kinds of machine learning models are studied: back propagation (BP) neural network, genetic algorithm‐back propagation (GA‐BP) neural network, radial basis function (RBF) neural network, then predictive models based on finite element method (FEM) and machine learning models are proposed. In the double‐dwell curing curve, six typical parameters are selected as inputs; the maximum value of temperature, the maximum value of temperature overshoot, the maximum DoC difference, the curing time, these four parameters during the curing process are selected as outputs, then the rapid predictive model is established. Within the value range of the process parameters, the Latin hypercube sampling (LHS) method is used to select 100 sets of sample points, and after training on three predictive models, comparison, and verification are carried out. The results show that the predictive effect of the RBF model is the best. In these three models, the RBF model is more suitable for the performance prediction of composites PCM. In this article, the research provides the basis for the performance prediction of composites and the multiobjective optimization of the curing process.
The insulation performance of the core transformer is determined by the winding quality of the insulation layer between the coils. It is necessary to assure uniform winding tension in order to ensure the insulation performance of the transformer and improve production efficiency. Thus, in this article, the dynamic characteristics of the transformer insulation winding process are analyzed, and the insulation winding tension control system is designed to decrease the tension fluctuation caused by the alternating change of the elliptical core radius in the winding process. Firstly, the time-varying characteristics of roll radius and inertia are analyzed, the mechanical structure and dynamic models of unwinding and rewinding are established, and the effects of upstream and downstream span length and dancer inertia on the resonant frequency and response performance of the system are revealed. Later, the influence factors of tension fluctuation and disturbance propagation frequency are investigated, and the tension control model of Hybrid dancer position feedback is established. The variable universe fuzzy PI controller is designed by setting fuzzy rules and universe adjustment factors. Finally, the stability of the controller and the weakening effect on the tension disturbance are verified by simulation, and the performance and application value of the automatic winding tension control system are tested by a series of experiments. INDEX TERMS Transformer insulation winding, tension control, disturbance, variable universe fuzzy PI, hybrid dancer.
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