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



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


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)
Conference Dates
June 27-July 1, 2016
Conference Location
Blacksburg, VA, US


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


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],, (Accessed May 23, 2024)


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Created August 4, 2016, Updated April 18, 2022