A Sensor-Based Method for Diagnostics of Machine Tool Linear Axes

Gregory W. Vogl, Brian A. Weiss, and M. Alkan Donmez
Submission Type: 
Full Paper
AttachmentSizeTimestamp
phmc_15_036.pdf1.02 MBOctober 1, 2015 - 11:30am

A linear axis is a vital subsystem of machine tools, which are vital systems within many manufacturing operations. When installed and operating within a manufacturing facility, a machine tool needs to stay in good condition for parts production. All machine tools degrade during operations, yet knowledge of that degradation is illusive; specifically, accurately detecting degradation of linear axes is a manual and time-consuming process. Thus, manufacturers need automated and efficient methods to diagnose the condition of their machine tool linear axes without disruptions to production. The Prognostics and Health Management for Smart Manufacturing Systems (PHM4SMS) project at the National Institute of Standards and Technology (NIST) developed a sensor-based method to quickly estimate the performance degradation of linear axes. The multi-sensor-based method uses data collected from a ‘sensor box’ to identify changes in linear and angular errors due to axis degradation; the sensor box contains inclinometers, accelerometers, and rate gyroscopes to capture this data. The sensors are expected to be cost effective with respect to savings in production losses and scrapped parts for a machine tool. Numerical simulations, based on sensor bandwidth and noise specifications, show that changes in straightness and angular errors could be known with acceptable test uncertainty ratios. If a sensor box resides on a machine tool and data is collected periodically, then the degradation of the linear axes can be determined and used for diagnostics and prognostics to help optimize maintenance, production schedules, and ultimately part quality.

Publication Year: 
2015
Publication Volume: 
6
Publication Control Number: 
036
Page Count: 
10
Submission Keywords: 
sensors
diagnostics
degradation
Linear axes
Machine tools
Submission Topic Areas: 
Data-driven methods for fault detection, diagnosis, and prognosis
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