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Shape control method of fuselage driven by digital twin |
Yong-sheng ZHAO1,2( ),Rui-xiang LI1,2,Na-na NIU1,3,Zhi-yong ZHAO1,3 |
1. Institute of Advanced Manufacturing and Intelligent Technology, Beijing University of Technology, Beijing 100020, China 2. Beijing Key Laboratory of Advanced Manufacturing Technology, Beijing University of Technology, Beijing 100020, China 3. Machinery Industry Key Laboratory of Heavy Machine Tool Digital Design and Testing Technology, Beijing University of Technology, Beijing 100020, China |
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Abstract A method of fuselage shape control based on digital twin drive was proposed in order to solve the problems of low precision, low efficiency and excessive local stress of manual adjustment. A digital twin system integrating shape control strategy optimization algorithm and virtual debugging technology was constructed. The digital twin system was driven by real-time data. The data interaction and the dynamic mapping between physical space and virtual space of fuselage shape control system were realized. The optimization problem of shape control strategy combining genetic algorithm and deep learning was analyzed. Then the availability of shape control strategy was verified through the ANSYS batch multi-load step method. The shape of fuselage was adjusted by maintaining the stress balance of fuselage barrel section. The experimental results show that the digital twin system and shape control strategy can effectively improve the control accuracy of fuselage barrel section shape by 25.8%, improve the control efficiency of fuselage barrel section shape by 414.3% and reduce the local maximum stress by 42.5%.
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Received: 24 December 2021
Published: 26 July 2022
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Fund: 国家自然科学基金资助项目(52075012);航天伺服驱动与传动技术实验室开放基金资助项目(LASAT-20210103) |
数字孪生驱动的机身形状控制方法
针对人工调整机身筒段形状过程中存在的精度低、效率低以及局部过大应力的问题,提出基于数字孪生驱动的机身形状控制方法,搭建融合形状控制策略优化算法和虚拟调试技术的数字孪生系统. 在实时数据的驱动下,实现机身形状控制系统物理空间与虚拟空间的数据交互、动态映射. 研究结合遗传算法和深度学习的形状控制策略优化问题,通过ANSYS批处理多载荷步的方法,验证形状控制策略的可用性,在保持机身筒段应力均衡的状态下调整机身形状. 实验结果表明,利用构建的数字孪生系统和形状控制策略,可以有效地将筒段形状控制精度提高25.8%,将筒段形状控制效率提高414.3%,将局部最大应力减小42.5%.
关键词:
数字孪生,
机身形状,
遗传算法,
深度学习,
策略优化
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