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

Published

Author(s)

Gregory W. Vogl, Matthew Calamari, Sean Ye, Alkan Donmez

Abstract

A linear axis is a vital subsystem of machine tools, and 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 elusive; accurate detection of linear axis degradation 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. Towards this end, a sensor-based method was developed to quickly estimate the performance degradation of linear axes. The multi-sensor-based method uses data from inclinometers, accelerometers, and rate gyroscopes to identify changes in linear and angular errors due to axis degradation. A linear axis testbed, developed for verification and validation of the sensor-based method, contains a linear axis and a reference laser-based system for measurement of the axis geometric performance. Comparison of the sensor-based results and the laser-based results shows that the sensor-based method is capable of detecting micrometer-level and microradian-level degradation of linear axes. Consequently, if a sensor box resides on a machine tool, then the degradation of the linear axes can be periodically measured and used to help optimize maintenance.
Proceedings Title
44th SME North American Manufacturing Research Conference (NAMRC)
Volume
5
Conference Dates
June 27-July 1, 2016
Conference Location
Blacksburg, VA, US

Keywords

Machine tool, Linear Axis, Error, Wear, Degradation, Sensor, Diagnostics, Maintenance

Citation

Vogl, G. , Calamari, M. , Ye, S. and Donmez, A. (2016), A Sensor-Based Method for Diagnostics of Geometric Performance of Machine Tool Linear Axes, 44th SME North American Manufacturing Research Conference (NAMRC), Blacksburg, VA, US, [online], https://doi.org/10.1016/j.promfg.2016.08.051, https://tsapps.nist.gov/publication/get_pdf.cfm?pub_id=920625 (Accessed May 23, 2024)

Issues

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Created August 4, 2016, Updated April 18, 2022