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Inertial Measurement Unit for On-Machine Diagnostics of Machine Tool Linear Axes

Published

Author(s)

Gregory W. Vogl, Alkan Donmez, Andreas Archenti, Brian A. Weiss

Abstract

Machine tools degrade during operations, yet knowledge of degradation is elusive; accurately detecting degradation of machines' components such as linear axes is typically a manual and time-consuming process. Thus, manufacturers need automated, efficient, and robust methods to diagnose the condition of their machine tool linear axes with minimal disruptions to production. Towards this end, a method was developed to use data from an inertial measurement unit (IMU) for identification of changes in the translational and angular errors due to axis degradation. The IMU-based method uses data from accelerometers and rate gyroscopes to identify changes in linear and angular errors due to axis degradation. A linear axis testbed, established for the purpose of verification and validation, revealed that the IMU-based method was capable of measuring geometric errors with acceptable test uncertainty ratios. Specifically, comparison of the IMU-based and laser-based results demonstrate that the IMU-based method is capable of detecting micrometer-level and microradian-level degradation of linear axes. Consequently, an IMU was created for application of the IMU-based method on a machine tool as a proof of concept for detection of linear axis error motions. If the data collection and analysis are integrated within a machine controller, the process may be streamlined for the optimization of maintenance activities and scheduling, supporting more intelligent decision-making by manufacturing personnel and the development of self-diagnosing smart machine tools.
Conference Dates
October 2-8, 2016
Conference Location
Denver, CO, US
Conference Title
2016 Annual Conference of the Prognostics and Health Management Society

Keywords

machine tool, linear axis, sensors, diagnostics

Citation

Vogl, G. , Donmez, A. , Archenti, A. and Weiss, B. (2016), Inertial Measurement Unit for On-Machine Diagnostics of Machine Tool Linear Axes, 2016 Annual Conference of the Prognostics and Health Management Society, Denver, CO, US, [online], https://tsapps.nist.gov/publication/get_pdf.cfm?pub_id=921215 (Accessed December 3, 2022)
Created October 7, 2016, Updated April 6, 2022