@article{noauthororeditor,
  abstract = {To ensure high surface quality and low costs during production milling processes, the tool wear has to be monitored constantly. However, manual inspection can be highly time consuming and erroneous. Simple automatic solutions are often not sufficient, since the tool wear behaves usually non-linear and depends on many factors. 

In this work, we successfully employed machine learning methods on microscope images of the tool and force/acceleration sensor data for indirect determination of tool wear. The data was generated in the lab using a CNC machine in a multi interpolation manufacturing process and three tool wear classes were identified by experts.},
  added-at = {2020-03-27T13:06:12.000+0100},
  address = {Lausanne, Switzerland},
  author = {Giri, Siddhartha and Truchan, Hubert and Rokicki, Markus and Zab, Jan-Hendrik and Noske, Hendrik and Niederée, Claudia and Denkena, Behrend and Nejdl, Wolfgang and Zerr, Sergej},
  biburl = {https://www.bibsonomy.org/bibtex/217634480ec0b15ca266ec8a216be1c4d/zerr},
  booktitle = {Applied Machine Learning Days},
  interhash = {e8d079a07f98b66a1363fa668039704d},
  intrahash = {17634480ec0b15ca266ec8a216be1c4d},
  keywords = {myown},
  organization = {EPFL},
  timestamp = {2020-03-27T13:06:12.000+0100},
  title = {Smart Manufacturing: Monitoring of Tool Wear using Machine Learning Methods},
  url = {https://appliedmldays.org/},
  year = 2018
}