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缠绕张力作为纤维缠绕成型中的关键影响因素,其波动直接影响缠绕精度和制品的性能。针对缠绕张力的动态变化,且保证制品等环向残余应力,提出神经网络动态控制缠绕变张力方法。考虑芯模变形影响,基于各向异性缠绕层弹性变形及各向同性内衬厚壁筒理论,给出外压作用下缠绕层的径向应力及环向应力;在弹性范围内采用应力叠加原理建立剩余张力与缠绕张力之间的解析算法。基于制品环向残余应力叠加特点,采用给定输出层权值的神经网络算法,通过误差反向传播及放大方法,对等环向残余应力制品纤维缠绕过程中的缠绕变张力进行动态更新。仿真与实验结果表明:该控制方法对纤维缠绕变张力起到动态优化作用,可以达到预期要求,且更符合实际缠绕过程。
Winding tension as a key factor in the filament winding process, its fluctuation directly affects the winding accuracy and product performance. Aiming at the dynamic change of the winding tension and the circumferential residual stress of the product, a neural network is proposed to dynamically control the winding tension. Considering the influence of the deformation of the mandrel, the radial stress and hoop stress of the wound layer under the external pressure are given based on the elastic deformation of the anisotropic wound layer and the isotropic lining thick wall cylinder theory. The principle of stress superposition is established in the elastic range Analytic algorithm between residual tension and winding tension. Based on the characteristics of product circumferential residual stress superposition, a neural network algorithm with given output layer weights was used to dynamically update the winding tension in the process of filament winding of residual stress products by error back propagation and amplification. The simulation and experimental results show that the proposed control method can dynamically optimize the fiber tension, which can meet the expected requirements and is more in line with the actual winding process.