Cutting tool condition monitoring using eigenfacesTool wear monitoring in milling

被引:0
|
作者
Wolfgang König
Hans-Christian Möhring
机构
[1] Institute for Manufacturing Technology and Quality Assurance (IFQ),Otto
[2] University Stuttgart,von
来源
Production Engineering | 2022年 / 16卷
关键词
Machine tool; Wear; Tool; Monitoring; Milling; Signal processing; Statistical process control; Principal component analysis; Eigenface; Sensor data fusion;
D O I
暂无
中图分类号
学科分类号
摘要
Effective monitoring of the tool wear condition within a machining process can be very challenging. Depending on the sensors used, often only a part of the relevant wear information can be detected. In the case of milling processes data acquisition is made even more difficult by the fact that the process working point is inaccessible for sensor applications due to the physical tool, the machining process itself, the chipping and used cooling-lubricants. By using a variety of sensors and different measuring principles, sensor data fusion strategies can counteract this problem. An approach to this is the eigenface algorithm. This approach, a face recognition technique, is tested for its suitability on tool condition monitoring in milling processes by using multi-sensor process data.
引用
收藏
页码:753 / 768
页数:15
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