Huang Lulu, Huang Xiang, Li Shuanggao, Hou Guoyi
College of Mechanical and Electrical Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, People's Republic of China.
Rev Sci Instrum. 2023 Jun 1;94(6). doi: 10.1063/5.0148804.
The combination of large tooling size, environmental vibration, and equipment errors at the aircraft assembly site leads to errors in the enhanced reference system (ERS) point measurement information. ERS point errors directly reduce the accuracy of the assembly measurement field. This paper proposes ERS point error prediction and registration compensation based on the neural network to address this problem. First, the effects of equipment measurement errors and environmental vibration factors on the measurement field are studied. The ERS point error prediction model based on the neural network is established. On this basis, model evaluation is used to assess the prediction model of this paper. Then, a measurement field registration compensation model is constructed based on the neural network error results for ERS point compensation analysis. Finally, an experimental validation platform was built to predict the ERS point errors and compensate for the constructed measurement fields using the method in this paper. The experimental results show that, compared with the conventional method, the maximum registration errors in the X, Y, and Z directions are reduced from 0.0812, -0.0565, and -0.2810 to -0.0184, -0.0010, and 0.0022 mm, respectively, after compensation in this paper. The method proposed in this paper can not only predict the ERS point error state and provide a reference for designers but also guide the selection of appropriate ERS points when constructing the measurement field. The compensation method in this paper effectively reduces the measurement field registration error.
飞机装配现场的大型工装尺寸、环境振动和设备误差相结合，导致增强参考系统（ERS）点测量信息出现误差。ERS点误差直接降低了装配测量场的精度。本文提出基于神经网络的ERS点误差预测与配准补偿方法来解决这一问题。首先，研究了设备测量误差和环境振动因素对测量场的影响。建立了基于神经网络的ERS点误差预测模型。在此基础上，通过模型评估来评价本文的预测模型。然后，基于神经网络误差结果构建测量场配准补偿模型，用于ERS点补偿分析。最后，搭建了实验验证平台，采用本文方法对ERS点误差进行预测，并对构建的测量场进行补偿。实验结果表明，与传统方法相比，本文补偿后在X、Y、Z方向上的最大配准误差分别从0.0812、 -0.0565和 -0.2810减小到 -0.0184、 -0.0010和0.0022毫米。本文提出的方法不仅可以预测ERS点误差状态，为设计人员提供参考，还可以在构建测量场时指导合适的ERS点的选择。本文的补偿方法有效降低了测量场配准误差。
