A study of tool tipping monitoring for titanium milling based on cutting vibration

In titanium milling machining, tool condition monitoring (TCM) is very important owing to the short tool life and expensive cost. And the TCM is the key technology for automated machining. In titanium milling, tipping is the main tool failure mode. In this paper, in order to monitor the tool tipping...

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Bibliographic Details
Published in:International journal of advanced manufacturing technology 2019-10, Vol.104 (9-12), p.3457-3471
Main Authors: Mou, Wenping, Jiang, Zhenxi, Zhu, Shaowei
Format: Article
Language:English
Subjects:
Accelerometers
CAE) and Design
Case studies
Computer-Aided Engineering (CAD
Cutting parameters
Engineering
Failure modes
False alarms
Industrial and Production Engineering
Low pass filters
Machinery condition monitoring
Mechanical Engineering
Media Management
Milling (machining)
Original Article
Power spectral density
Segmentation
Signal monitoring
Titanium
Tool life
Vibration analysis
Vibration monitoring
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Summary:In titanium milling machining, tool condition monitoring (TCM) is very important owing to the short tool life and expensive cost. And the TCM is the key technology for automated machining. In titanium milling, tipping is the main tool failure mode. In this paper, in order to monitor the tool tipping in practical production of complex titanium parts, a cutting vibration signal–based segmented monitoring method is proposed. An accelerometer mounted on the spindle is used to sense the cutting vibration. The undesired signal during air-cut is analyzed and eliminated by low-pass filtering. Increments of the moving average root mean square (MARMS) and peak power spectral density (PPSD) are extracted as indicators in time domain and frequency domain respectively. In addition, in order to eliminate the effect caused by continuously changed cutting condition in complex machining operations to reduce false alarms, a segmented monitoring strategy and corresponding NC block segmentation method are proposed. Finally, a framework of an online monitoring is built up. A case study shows that continuous tipping can also be detected, and the proposed method is effective for different cutting parameters.
ISSN:0268-3768
1433-3015
DOI:10.1007/s00170-019-04059-5