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Smart Machine Health and Maintenance: Tool Assembly Prognostics

[+] Author Affiliations
Edzel R. Lapira, Jay Lee

University of Cincinnati, Cincinnati, OH

Amit Deshpande, John Snyder

TechSolve, Inc., Cincinnati, OH

Paper No. MSEC_ICMP2008-72314, pp. 75-83; 9 pages
  • ASME 2008 International Manufacturing Science and Engineering Conference collocated with the 3rd JSME/ASME International Conference on Materials and Processing
  • ASME 2008 International Manufacturing Science and Engineering Conference, Volume 2
  • Evanston, Illinois, USA, October 7–10, 2008
  • Conference Sponsors: Manufacturing Engineering Division
  • ISBN: 978-0-7918-4852-4 | eISBN: 978-0-7918-3836-6
  • Copyright © 2008 by ASME


It is well-established that unbalance in tool assembly causes excessive loads on spindle bearings and tool wear and increased vibration levels. However, in the days where high-speed machining (HSM) has become a common practice in the manufacturing industry, methodologies to measure tool assembly unbalance are not developed. In HSM the effects are worse, as the unbalance force is directly proportional to square of the spindle speed. Common practice in industry is to balance the tool assembly either with in-house balancing machines or use third-party balancing services after every batch cycle. This paper describes a data-driven methodology that detects the presence of unbalance in a tool assembly relative to the tools with known balance levels. The unbalance detection prognostic application developed as part of the Smart Machine Platform Initiative (SMPI) checks for the threshold unbalance level in the tool assembly for the given machining requirements before the start of any run. This approach uses statistical tools and a supervised learning algorithm based on the Watchdog Agent® toolbox developed by the Center for Intelligent Maintenance Systems. The proposed research finds high applicability in high-precision manufacturing operations involving high-volume production.

Copyright © 2008 by ASME



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