Tool wear predictability estimation in milling based on multi-sensorial data

  • P. Stavropoulos
  • A. Papacharalampopoulos
  • E. Vasiliadis
  • G. ChryssolourisEmail author
Open Access


The safe and reliable operations in industrial manufacturing processes play a crucial role in the economic productivity. Machining process disturbances such as collision, overload, breakdown, and tool wear tend to cause production system failures. The current study aims at investigating the limitations of tool wear prediction on the milling of CGI 450 plates, through the simultaneous detection of acceleration and spindle drive current sensor signals. Tool wear prediction has been accomplished, by utilizing the experimental results that derived from third degree regression models and pattern recognition systems. These results indicate that predictability is affected by the mean signal energy, acquired from the vibration acceleration signals.


Tool wear Monitoring Predictability Fused sensorial signals Pattern recognition 


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© The Author(s) 2015

Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (, which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.

Authors and Affiliations

  • P. Stavropoulos
    • 1
    • 2
  • A. Papacharalampopoulos
    • 1
  • E. Vasiliadis
    • 1
  • G. Chryssolouris
    • 1
    Email author
  1. 1.Laboratory for Manufacturing Systems and Automation, Department of Mechanical Engineering and AeronauticsUniversity of PatrasPatrasGreece
  2. 2.Department of Aeronautical Studies, Hellenic Air Force AcademyDekelia Air-Force BaseAthensGreece

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