一个用于工业CNC削过程监控和异常检测的多模式数据集
Robin Ströbel1, Maximilian Kuck1, Florian Oexle1
1wbk Institute of Production Science, Karlsruhe Institute of Technology (KIT), 76131 Karlsruhe, Germany.
Data in brief
|November 19, 2025
概括
来自现实削工艺的新数据集使得用于敏捷制造的机器学习 (ML) 的基准测试成为可能. 该资源有助于验证ML监控系统,弥合了研究和工业应用之间的差距.
科学领域:
- 制造业 工程 制造工程
- 数据科学数据科学数据科学
- 工业自动化 工业自动化
背景情况:
- 敏捷生产和产品个性化在第四次工业革命中至关重要.
- 传统的过程监控对于灵活的制造是不够的,阻碍了机器学习 (ML) 的采用.
- 缺乏系统可比性和现实的验证,阻碍了ML在工业实践中的广泛应用.
研究的目的:
- 创建一个全面的,可重复的数据集,用于基于ML的敏捷流程监控的基准测试.
- 促进在现实的生产条件下对监控系统的评估和优化.
- 弥合在机器学习研究和制造业中工业化实施之间的差距.
主要方法:
- 在Deckel Maho DMC 60 H机上以高频率 (500 Hz,10 kHz) 记录多信号数据 (控制器,力,加速度).
- 进行了32个实验,包括15个具有8种不同的异常类型的实验,每信号产生约800万个数据点.
- 通过提供NC代码,CAD模型和各种数据格式 (.json, .mat, .csv, .stp, .nc) 确保了完全可复制性.
主要成果:
- 一个丰富的数据集,具有现实的工件几何形状和多种异常类型,用于有针对性的验证.
- 六小时的同步工艺数据捕捉了各种削场景和潜在的缺陷.
- 一个基准资源,可以直接比较和评估不同的过程监控方法.
结论:
- 该数据集是敏捷流程监控中的工业利益相关者和研究人员的关键基准.
- 它促进了机器学习模型的验证和优化,以应对现实世界制造业的挑战.
- 通过促进可比性,数据集旨在加速工业中采用先进的监控解决方案.
相关概念视频
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Zero-sequence current induces a voltage drop across the generator's neutral impedance and other...
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