一个新的开放数据集从一个削过程 - 数据用于分类和估计工具寿命的数据
Grzegorz Piecuch1, Tomasz Żabiński2
1Department of Computer and Control Engineering, Faculty of Electrical and Computer Engineering, Rzeszow University of Technology, Rzeszow, Poland. gpiecuch@prz.edu.pl.
Scientific data
|April 17, 2025
概括
本研究介绍了CNC加工工具故障的振动和当前数据. 这些数据可以通过分类工具状况和预测故障来实现预测性维护.
科学领域:
- 制造业 工程 制造工程
- 机械工程 机械工程
- 数据科学数据科学数据科学
背景情况:
- 状态监控对于优化CNC加工操作至关重要.
- 预测性维护系统需要全面的数据集来进行准确的故障诊断.
- 了解工具磨损和故障机制对于工业效率至关重要.
研究的目的:
- 引入并向公众提供振动数据集和CNC加工的当前数据.
- 提供数据,涵盖14种切削工具的整个生命周期,直到故障.
- 促进工具状况分类和故障预测方面的研究.
主要方法:
- 数据从哈斯VF-1数控机器采用贝克霍夫PAC系统收集.
- 在42CrMo4材料的顺时针削过程中,记录了14种切削工具的振动和电流数据.
- 数据包括968个削周期,包括原始和聚合数据与元数据.
主要成果:
- 一个全面的数据集,详细介绍了工具磨损到故障的进展.
- 数据集包括与工具状况相关的同步振动和电流测量.
- 元数据为实验条件和工具使用提供了上下文.
结论:
- 该数据集对于开发和验证用于智能预测维护的算法非常有价值.
- 它支持实时工具状况监测和剩余使用寿命估计的研究.
- 公共可用性促进了数据驱动制造优化方面的进步.
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