在制造业中,基于知识图的几何特征提取
Tobias Köhler1,2, Buchao Song2, Jean Pierre Bergmann2
1German Aerospace Center. (DLR), Institute of Data Science, Jena, Germany.
Heliyon
|October 9, 2023
概括
智能制造通过自动化决策来提高生产链的弹性. 本研究开发了3D模型的自动特征识别 (AFR),以实现更快,数据驱动的流程规划.
科学领域:
- 制造业 工程 制造工程
- 计算机辅助设计 (CAD)
- 工业4.0 工业4.0 工业4.0 工业4.0 工业4.0 是什么?
背景情况:
- 全球危机凸显了对弹性生产链的需求.
- 智能制造和工业4.0为自动化决策提供解决方案.
- 计算机辅助过程规划 (CAPP) 连接了设计和制造阶段.
研究的目的:
- 开发一种自动特征识别 (AFR) 方法.
- 从3D模型中识别几何制造特征和属性.
- 通过分析设计数据来实现自动化流程规划.
主要方法:
- 使用Python脚本从3D模型 (STEP-AP242格式) 中提取几何和拓数据.
- 将提取的数据存储在基于本体学的知识库中.
- 对产品数据的分析,以识别具有分层,分层结构的制造特征.
主要成果:
- 成功开发了一种自动特征识别 (AFR) 方法.
- 创建一个存储组件特征和属性的知识库.
- 证明了自动化分析和设计与制造信息比较的潜力.
结论:
- 开发的AFR方法有助于自动化流程规划.
- 数字化和产品生命周期阶段的联系对于智能制造至关重要.
- 这种方法提高了生产规划的效率和弹性.
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