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基于神经网络的大规模测量场的注册错误补偿的研究

Lulu Huang¹, Xiang Huang¹, Shuanggao Li¹

¹College of Mechanical and Electrical Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, People's Republic of China.

The Review of scientific instruments

|October 20, 2023

概括

本研究介绍了一种神经网络方法,用于预测和补偿增强参考系统 (ERS) 点中的错误,从而提高飞机组装的准确性. 与传统技术相比,这种方法显著减少了测量现场记录错误.

科学领域:

航空航天工程航空航天工程
计量学计量学是一门学科.
机器学习机器学习

背景情况:

飞机组装依赖于精确的测量场,往往由于增强参考系统 (ERS) 点的错误而受到损害.
诸如大型工具,环境振动和设备故障等因素导致ERS点的不准确性,从而降低了整体组件的测量准确性.

研究的目的:

提出和验证一种基于神经网络的方法,用于预测增强参考系统 (ERS) 点错误.
使用预测的ERS点误差开发测量场的补偿模型.
通过减轻ERS点误差,提高飞机组件测量的准确性.

主要方法:

研究了设备错误和环境振动对测量领域的影响.
开发了一个神经网络模型用于ERS点误差预测.
基于神经网络输出构建了一个测量场注册补偿模型.
使用实验平台验证了该方法.

主要成果:

提出的方法显著减少了X,Y和Z方向的最大注册误差,分别从0.0812,-0.0565,-0.2810毫米减少到-0.0184,-0.0010,0.0022毫米.
证明有效地补偿测量现场记录错误.

结论:

神经网络方法准确地预测了ERS点错误,帮助设计人员和ERS点选择.
补偿方法有效地减少了测量现场注册错误,提高了飞机组装精度.

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