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Experimental Procedure for Warm Spinning of Cast Aluminum Components
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A defect-driven diagnostic method for machine tool spindles.

Gregory W Vogl1, M Alkan Donmez1

  • 1Engineering Laboratory, National Institute of Standards and Technology (NIST), 100 Bureau Drive, Gaithersburg, Maryland 20899-8220, USA.

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|January 10, 2017
PubMed
Summary
This summary is machine-generated.

Simple vibration metrics fail to diagnose machine tool spindle health. A new spindle condition estimation device (SCED) separates system dynamics from defect geometry, enabling defect-driven diagnostics for robust machine tool analysis.

Keywords:
Condition monitoringMachine toolsSpindleVibration

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Area of Science:

  • Mechanical Engineering
  • Manufacturing Technology
  • Condition Monitoring

Background:

  • Traditional vibration-based metrics often inadequately diagnose machine tool spindle condition.
  • Existing methods can conflate defect-induced motion with inherent spindle dynamics, hindering accurate diagnosis.
  • A defect-driven approach is crucial for precise spindle health assessment.

Purpose of the Study:

  • To develop a novel method for diagnosing machine tool spindle condition.
  • To introduce a spindle condition estimation device (SCED) for data acquisition and analysis.
  • To propose a new spindle condition metric based solely on defect geometry.

Main Methods:

  • Development of a new data acquisition and analysis method.
  • Implementation of a spindle condition estimation device (SCED).
  • Separation of system dynamics from defect geometry in acquired data.
  • Proposal of a novel spindle condition metric derived from defect geometry.

Main Results:

  • The developed SCED effectively acquires relevant data.
  • The method successfully separates system dynamics from defect geometry.
  • The proposed defect geometry-based metric provides a clear indication of spindle condition.
  • Successful application on diverse milling and turning spindles demonstrates robustness.

Conclusions:

  • The new method and SCED offer a robust solution for machine tool spindle diagnostics.
  • Focusing on defect geometry provides a more accurate and reliable condition assessment.
  • This approach overcomes limitations of simple vibration-based metrics.