Condition monitoring of ball screw feed drives using convolutional neural networks

Maximilian Benker, Michael F. Zaeh

Research output: Contribution to journalArticlepeer-review

8 Scopus citations

Abstract

Ball screw feed drives are widely used in machine tools and significantly determine the manufacturing quality and efficiency. With their degradation, machining accuracy and economic efficiency decrease. Therefore, monitoring the condition of ball screws is of great interest. Past investigations showed that condition monitoring of ball screws is possible. Nevertheless, practical applications of a condition monitoring system for ball screw drives are rare, as it is unclear how well they perform on unseen components. In this paper a data-driven approach is presented, which can assess the condition of unseen ball screws with an accuracy of up to 98%.

Original languageEnglish
Pages (from-to)313-316
Number of pages4
JournalCIRP Annals - Manufacturing Technology
Volume71
Issue number1
DOIs
StatePublished - Jan 2022

Keywords

  • Artificial neural network
  • Ball screw drive
  • Condition monitoring
  • Machine tool

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