多变量时间序列数据的加工过程与不同的工具磨损和机床工具
Berend Denkena1, Heinrich Klemme1, Tobias H Stiehl1
1Institute of Production Engineering and Machine Tools, Leibniz Universität Hannover, 30823 Garbsen, Germany.
Data in brief
|October 9, 2023
概括
本研究引入了一套用于监测加工工具磨损的新数据集. 这些数据使研究能够预测工具寿命,并优化制造过程的维护策略.
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
- 材料科学 材料科学 材料科学
背景情况:
- 切割工具磨损是制造过程中的关键因素,影响产品质量和工艺稳定性.
- 目前基于固定时间表的工具更换方法导致工具使用寿命不足和成本增加.
- 开发有效的工具磨损监测系统需要广泛的标记数据集,这些数据目前很少.
研究的目的:
- 为了呈现一个全面的,标记的削工艺的数据集,与不同的工具磨损.
- 促进工具状况监测和预测性维护方面的研究.
- 能够识别易磨损的信号特征,并开发工具寿命预测模型.
主要方法:
- 在不同的机床中使用相同的工艺参数记录削操作的多变量时间序列数据.
- 使用九个固体碳化物末端削刀,在铁的肩部削中磨损到使用寿命结束 (VB ≈ 150 μm).
- 收集工艺力 (25 kHz) 和机器控制数据 (500 Hz),包括线/料驱动力/扭矩和位置控制偏差.
- 标记6,418个数据文件,其中包括磨损 (VB),机床 (M),工具 (T),运行 (R) 和累计工具接触时间 (C).
主要成果:
- 一个独特的,标记的削过程数据数据集,捕捉工具磨损的进展,现在是公开的.
- 该数据集包括高频力数据和低频机器控制数据,为分析提供丰富的信息.
- 数据涵盖了多个工具批和机床,允许进行可靠的模型开发和验证.
结论:
- 这一数据集是推动工具状况监测和预测性维护研究的宝贵资源.
- 它支持对用于提取磨损特征的信号处理技术的调查.
- 这些数据可以用于开发和测试算法,以准确地估计工具磨损和预测剩余的使用寿命,最终提高制造效率.
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