@inproceedings{580,
  abstract     = {{Additive Manufacturing (AM) is increasingly used to design new products. This is possible due to the further development of the AM-processes and materials. The lack of quality assurance of AM built parts is a key technological barrier that prevents manufacturers from adopting. The quality of an additive manufactured part is influenced by more than 50 parameters, which make process control difficult. Current research deals with using real time monitoring of the melt pool as feedback control for laser power. This paper illustrates challenges and opportunities of applying statistical predictive modeling and unsupervised learning to control additive manufacturing. In particular, an approach how to build a feedforward controller will be discussed.}},
  author       = {{Scheideler, Eva and Ahlemeyer-Stubbe, Andrea}},
  booktitle    = {{	 Production engineering and management : proceedings 7th international conference, September 28 and 29, 2017, Pordenone, Italy }},
  editor       = {{Padoano, Elio and Villmer, Franz-Josef}},
  isbn         = {{978-3-946856-01-6}},
  keywords     = {{Additive manufacturing, Process control, Predictive modeling, Predictive control}},
  location     = {{Pordenone, Italy}},
  number       = {{1}},
  pages        = {{3--12}},
  title        = {{{Quality Control of Additive Manufacturing Using Statistical Prediction Models}}},
  volume       = {{2017}},
  year         = {{2017}},
}