Inertial Measurement Unit for On-Machine Diagnostics of Machine Tool Linear Axes
Gregory W. Vogl, Alkan Donmez, Andreas Archenti, Brian A. Weiss
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.
October 2-8, 2016
Denver, CO, US
2016 Annual Conference of the Prognostics and Health Management Society
, Donmez, A.
, Archenti, A.
and Weiss, B.
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)