Diagnostics of Machine Tool Linear Axes for Smart Manufacturing

Graduate #110
Discipline: Technology and Engineering
Subcategory: Civil/Mechanical/Manufacturing Engineering

Matlock M. Mennu - University of Florida

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Machine tools are essential in the manufacturing of various components in the automotive and aircraft manufacturing industry. Degradation of machine tool linear axes has a huge impact on the quality of parts manufactured through this process. Billions of US dollars are lost every year due to degradation of machine tools during production. Currently, there are direct methods of measuring geometric errors using laser-based and other standard methods. However, these methods require the machine to be taken offline during testing. This process is time-consuming, complicated, and halts production, all of which usually equates to lost revenue making it less desirable to manufacturers. This project uses an inertial measurement unit (IMU), an in-situ testing device, that
seeks to overcome some of the barriers of traditional test equipment. The IMU uses relatively cheap sensors to
measure changes in geometric errors of linear axes efficiently and accurately. Previously quantification of the
standard measurements and accuracy of the IMU was performed. The current phase of the project focuses on
performing verification and validation of the IMU-based method with various types of degradation and creating
metric indicators of accumulated degradation.
A common degradation mechanism that can occur during machine tool operations is pitting or spalling. For this
study, one rail of the linear axis testbed was mechanically degraded to simulate this form of degradation. IMU and

laser-based reference data were collected in discrete steps of increased degradation that went from nominal state
(no degradation) to the final state (a failure state of the rail). The contribution of geometric errors from the rail-
based degradation were then separated with a technique developed in MATLAB that utilizes the various data for
each run. Diagnostic metrics were then defined for use with the IMU to help inform end-users of the magnitude
and location of wear and any violations of performance tolerances. The angular and translational metric values
calculated from the IMU show a good agreement of over 99% correlation to the reference data, verifying how the
IMU-based method can be used for degradation tracking within future smart machine tools. Future work involves creating algorithms that can provide prognostics of machine tool health and creating a Graphic User Interface(GUI) for the IMU system that allows any machinist to use this tool.

Funder Acknowledgement(s): National Institute of Standards and Technology

Faculty Advisor: Greg Vogl, gvogl@vt.edu

Role: My research involved developing the whole Matlab algorithm for the analysis of the Reference data and the new sensor data. I developed several functions that were involved in creating the diagnostic metrics derived from accelerometers and gyroscope data from the Inertial Measurement Unit(IMU).