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PUBLICATIONS

Identification of machine tool geometric performance using on-machine inertial measurements

Published

Author(s)

Gregory W. Vogl, Radu Pavel, Andreas Archenti, Thomas J. Winnard, Matlock M. Mennu, Brian A. Weiss, Alkan Donmez

Abstract

Machine tools degrade during operations, yet accurately detecting degradation of machine components such as linear axes is typically a manual and time-consuming process. Thus, manufacturers need automated and efficient methods to diagnose the condition of their machine tool linear axes with minimal disruptions to production. Towards this goal, a method was developed to use accelerometer and rate gyroscope data from an inertial measurement unit (IMU) for identification of changes in the translational and angular errors due to axis degradation. An IMU was created for application of the method on a machine tool. As a proof of concept for detection of translational error motions, IMU data was collected on a machine tool with experimentally simulated degradation; as the worktable moved along its nominal path, a cross-axis moved along a swept sinusoidal pattern with micrometer-level amplitudes. In another experiment, data was collected at three different locations on a worktable for the same axis motion. These experiments showed that the IMU detected micrometer-level and microradian-level degradation of linear axes, revealing that the IMU-based method is plausible for use in smart machine tools.
Proceedings Title
6th International Conference on Virtual Machining Process Technology (VMPT 2017)
Conference Dates
May 28-June 2, 2017
Conference Location
Montreal, CA
Pub Type
Conferences

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Keywords

Machine tool, Linear Axis, Error, Degradation, Diagnostics
Sensors, Metrology, Manufacturing and Machining

Citation

Vogl, G. , Pavel, R. , Archenti, A. , Winnard, T. , Mennu, M. , Weiss, B. and Donmez, A. (2017), Identification of machine tool geometric performance using on-machine inertial measurements, 6th International Conference on Virtual Machining Process Technology (VMPT 2017), Montreal, CA, [online], https://tsapps.nist.gov/publication/get_pdf.cfm?pub_id=923274 (Accessed October 11, 2025)

Issues

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Created June 6, 2017, Updated April 18, 2022
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