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Machine Condition Detection for Milling Operations Using Low Cost Ambient Sensors

[+] Author Affiliations
Anantha Narayanan, Alec Kanyuck, Satyandra K. Gupta

University of Maryland, Gaithersburg, MD

Sudarsan Rachuri

Department of Energy, Washington, DC

Paper No. MSEC2016-8666, pp. V002T04A005; 10 pages
  • ASME 2016 11th International Manufacturing Science and Engineering Conference
  • Volume 2: Materials; Biomanufacturing; Properties, Applications and Systems; Sustainable Manufacturing
  • Blacksburg, Virginia, USA, June 27–July 1, 2016
  • Conference Sponsors: Manufacturing Engineering Division
  • ISBN: 978-0-7918-4990-3
  • Copyright © 2016 by ASME


In recent years, sensor technology and data mining capabilities have advanced greatly, allowing advanced manufacturing enterprises to closely monitor their manufacturing operations. At the same time, a thriving market has developed for low cost consumer level sensors and processors. A proliferation of low cost sensing hardware, combined with the availability of free and open source software for performing data analytics, provides a new opportunity for smaller manufacturers. Yet, these tools have not been investigated deeply in the manufacturing world. In this work, we use a combination of low cost sensing hardware and free and open source software to monitor a milling machine operation. We demonstrate that the data collected from these sensors can be used to reliably determine the operating condition of the machine. These techniques will be very valuable for small manufacturers, to determine key factors such as machine utilization, or to detect catastrophic failures early during machining.

Copyright © 2016 by ASME
Topics: Machinery , Sensors , Milling



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