缺陷检测,以提高船舶建设中的可追溯性
Paula Arcano-Bea1, Manuel Rubiños1, Agustín García-Fischer1
1Department of Industrial Engineering, University of A Coruña, CTC, CITIC, 15403 Ferrol, Spain.
Sensors (Basel, Switzerland)
|February 26, 2025
概括
本研究介绍了使用无监督学习和卷积自动编码器 (CAE) 的小型造船部件的智能缺陷检测方法. 该方法通过自动识别预装配中的缺陷来加强船舶建造的质量控制.
科学领域:
- 海军建筑和海洋工程
- 数字制造 数字制造 数字制造
- 人工智能在质量控制中的作用
背景情况:
- 数字化正在改变造船业,强调质量保证和效率的可追溯性.
- 缺陷可追溯性对于识别和解决复杂的海军建筑中的问题至关重要.
- 轻微的预装配是基础的,但如果不被发现,这些缺陷可能会升级.
研究的目的:
- 提出一种智能自动化方法,用于检测船舶制造业中小型预装配件的缺陷.
- 实施无监督学习,使用卷积自动编码器 (CAE) 来识别缺陷.
- 评估各种CAE模型在检测超射缺陷方面的有效性.
主要方法:
- 利用无监督学习与卷积自动编码器 (CAE) 实现自动缺陷检测.
- 专注于识别小,简单的预装配件中的超射缺陷.
- 评估了五种不同的 CAE 架构:BaseLineCAE,InceptionCAE,SkipCAE,ResNetCAE 和 MVTecCAE.
主要成果:
- 证明了使用CAE用于在造船预装配件中自动检测缺陷的可行性.
- 为此特定应用提供了不同CAE模型的比较分析.
- 强调了通过人工智能实现可扩展和高效的质量控制的潜力.
结论:
- 使用CAE进行无监督学习为关键造船部件的缺陷检测提供了一个有希望的解决方案.
- 自动缺陷识别提高了海军建筑中的可追溯性和结构完整性.
- 这种方法支持数字造船时代的高效质量控制.
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