卷积神经网络用于航空航天系统中洞检查
Garrett Madison1, Grayson Michael Griser1, Gage Truelson1
1Lyle School of Engineering, Southern Methodist University, Dallas, TX 75205, USA.
Sensors (Basel, Switzerland)
|September 27, 2025
概括
在航空航天制造业中,外来物体碎片 (FOd) 检测得到了HANNDI的改进,HANNDI是一个使用深度学习进行快速,准确的检查的手持设备. 这种自动化光学检查系统显著减少了工厂的错误和检查时间.
科学领域:
- 航空航天工程 航空航天工程
- 计算机视觉 计算机视觉
- 制造业 制造技术 制造技术
背景情况:
- 在航空航天制造业中,手动检查外来物体碎片 (FOd) 是耗时的,令人疲劳的,容易出错的.
- 目前使用手电筒和镜子进行视觉检查的方法缺乏可靠性和效率.
研究的目的:
- 为了介绍HANNDI,这是一款新的手持式自动化光学检查系统.
- 为了在工厂地板上快速,可靠和自动检查孔,机械加工孔和紧固件部位的FOd.
主要方法:
- 开发一个紧的手持设备,集成可控光学,照明和内置深度学习 (基于YOLO的CNN).
- 实现焦点扫描,图像对齐和融合,以实现全焦的表示.
- 使用双重CNN管道用于洞探测/定位和碎片分类.
主要成果:
- HANNDI在航空航天资产的大型专有数据集上实现了近95%的每类精度和回忆.
- 在对飞机部件的端到端测试中,证明了每洞13.6秒的有效任务时间.
- 所有的训练数据都与原型一起收集,确保了一致的成像条件.
结论:
- 汉迪代表了第一个手持式自动化光学检查系统,具有机械几何强制执行,可控照明和嵌入式CNN推理.
- 该系统为强大的工厂地面部署提供了实用解决方案,增强了航空航天制造业的质量控制.
- 这项技术显著提高了外来物体碎片检查的速度和准确性.
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