Machine tools degrade during operations, yet knowledge of degradation is elusive; accurately detecting degradation of 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 an inertial measurement unit (IMU) composed of accelerometers and rate gyroscopes for identification of changes in translational and angular errors. In this talk, an IMU-based method will be presented that is capable of detecting micrometer-level and microradian-level degradation of linear axes. An IMU was created for application of the IMU-based method as a proof of concept for on-machine detection of linear axis error motions. Initial results revealed that the IMU-based method is capable of measuring geometric errors with acceptable test uncertainty ratios. Furthermore, if the data collection and analysis are integrated within a machine controller, the process may be streamlined for the optimization of maintenance activities and the development of self-diagnosing smart machine tools.
Dr. Gregory W. Vogl is a Mechanical Engineer at the National Institute of Standards and Technology (NIST) located in Gaithersburg, Maryland. He received his Ph.D. in Engineering Mechanics from Virginia Tech and is currently a member of the Prognostics, Health Management, and Control (PHMC) project and the Production Systems group in the Engineering Laboratory at NIST. Greg’s interests include diagnostic and prognostic methods, nonlinear dynamics, engineering mechanics, and metrology.