Identification of machine tools linear axes performance using on-machine embedded inertia measurement units

Published

March 15, 2017

Author(s)

Gregory W. Vogl, Andreas Archenti, Alkan Donmez

Abstract

The current trend in manufacturing industry is from mass production towards flexible and adaptive manufacturing systems and cloud manufacturing. Self-learning machines and robot systems can play an essential role in the development of intelligent manufacturing systems and can be deployed to deal with a variety of tasks that can require flexibility and accuracy. However, in order for the machine tool (physical and control system) to deal with the desired task in a cognitive and efficient manner, the system must be "aware" of its capability and, most importantly, its limitations in order to avoid them and adjust itself to the desired task. Thus, characterization of machine tool accuracy and capability is necessary to realize that. In this study, data from a machine-embedded inertial measurement unit (IMU), consisting of accelerometers and rate gyroscopes, was used for identification of changes in linear and angular error motions due to changes in operational conditions or component degradation. The IMU-based results were validated against laser-based measurement results, demonstrating that the IMU-based method is capable of detecting micrometer-level and microradian-level degradation of machine tool linear axes. Thus, manufacturers could use the method to efficiently and robustly diagnose the condition of their machine tool linear axes with minimal disruptions to production.

Proceedings Title

Lamdamap 12th International Conference & Exhibition

Conference Dates

March 15-16, 2017

Conference Location

Gloucestershire, UK

Pub Type

Conferences

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Citation

Vogl, G. , Archenti, A. and Donmez, A. (2017), Identification of machine tools linear axes performance using on-machine embedded inertia measurement units, Lamdamap 12th International Conference & Exhibition, Gloucestershire, UK, [online], https://tsapps.nist.gov/publication/get_pdf.cfm?pub_id=923275 (Accessed October 7, 2024)