材料工程与机械制造

圆筒纤维缠绕变张力神经网络动态控制

  • 康超 ,
  • 史耀耀 ,
  • 何晓东 ,
  • 张军 ,
  • 张晓扬
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  • 西北工业大学 现代设计与集成制造技术教育部重点实验室, 西安 710072
康超 男, 博士研究生。主要研究方向: 复合材料纤维缠绕工艺及装备控制。Tel: 029-88492851 E-mail: kaochao_017@163.com;史耀耀 男, 博士, 教授, 博士生导师。主要研究方向: 复合材料纤维缠绕铺放工艺及装备控制、数控装备技术及加工表面光整。Tel: 029-88492851 E-mail: shiyy@nwpu.edu.cn

收稿日期: 2014-05-08

  修回日期: 2014-06-30

  网络出版日期: 2014-07-26

基金资助

国家自然科学基金 (51375394)

Variable tension dynamic control for filament winding of cylinder using neural network

  • KANG Chao ,
  • SHI Yaoyao ,
  • HE Xiaodong ,
  • ZHANG Jun ,
  • ZHANG Xiaoyang
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  • The Key Laboratory of Contemporary Design and Integrated Manufacturing Technology, Ministry of Education, Northwestern Polytechnical University, Xi'an 710072, China

Received date: 2014-05-08

  Revised date: 2014-06-30

  Online published: 2014-07-26

Supported by

National Natural Science Foundation of China (51375394)

摘要

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。

本文引用格式

康超 , 史耀耀 , 何晓东 , 张军 , 张晓扬 . 圆筒纤维缠绕变张力神经网络动态控制[J]. 航空学报, 2015 , 36(4) : 1339 -1347 . DOI: 10.7527/S1000-6893.2014.0133

Abstract

As the key influencing factor in filament winding process, fluctuation of winding tension directly affects winding precision and productions' performance. In view of the dynamic change of winding tension and ensuring uniform circumferential residual stress of product, the method to dynamically control winding variable tension using a neural network is proposed. And considering the deformations of mandrel, the radial and circumferential stresses in winding layer under external pressure are obtained through analyzing the basis of anisotropic composite elastic theory and isotropic thick-walled cylinder elastic theory. Within the scope of the elastic limit, the analytic algorithm between residual tension distribution and winding tension is established based on the stress superposition principle. Based on the superposed characteristic of uniform circumferential residual stress, the variable tension during the winding process can be updated dynamically using a neural network with a given weight of output layer and error back propagation and amplification. Simulation and experimental results show that the proposed control method can dynamically optimize the variable tension of filament winding, and it can satisfy the desired requirements and is in line with the actual process of filament winding.

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