基于使用CAD的神经网络模型对飞机零件成型工具类型进行分类的数据集
Lee DongGyu1, Lee HyunSup2, Shin JaeHo2
1Korea Aerospace Industries, Ltd., 108, Gongdan 1-ro, Sanam-myeon, Sacheon, Republic of Korea.
Data in brief
|December 16, 2025
概括
本研究介绍了一种基于CAD图像的深度学习模型,用于分类飞机零件成型工具类型. 该模型达到96%的准确性,有助于小批量制造.
科学领域:
- 工程 工程师 工程师 工程师
- 计算机科学 计算机科学
- 材料科学 材料科学 材料科学
背景情况:
- 飞机结构在很大程度上依赖于金属板组件,通常使用流体细胞水电成型制造.
- 分类成型工具类型对于高效生产至关重要,特别是在各种制造场景中.
研究的目的:
- 开发和验证基于CAD图像的深度学习和机器学习模型,用于分类用于飞机零件制造的成型工具类型.
- 创建一个全面的CAD图像数据集,用于培训和评估这些模型.
主要方法:
- 从公开和生成的来源编译了12432张CAD图像的数据集,具有各种可视化模式 (正常,隐藏线,线框) 和定向.
- 图像根据边缘几何学被分为六种工具类型.
- 卷积神经网络 (CNN) 架构和机器学习模型对分类性能进行了评估.
主要成果:
- 使用正常视图与线框视图,ResNeXt模型实现了96%的最高分类准确率.
- 进一步评估了性能,使用准确度和F1得分来考虑类不平衡.
结论:
- 开发的基于CAD图像的模型为分类飞机零件成型工具提供了一个可扩展,具有成本效益和实用的解决方案.
- 这种方法对于小批量,多品种制造环境特别有利,减少了对物理原型或专用传感器的需求.
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