@inproceedings{ReserReich2019,
  author    = {Christian Reser and Christoph Reich},
  title     = {Grinding Burn Prediction with Artificial Neural Networks based on Grinding Parameters},
  series = {Smart SysTech 2019 : European Conference on Smart Objects, Systems and Technologies : June 04- 05, 2019, Magdeburg, Germany},
  publisher = {VDE Verlag},
  address   = {Berlin},
  isbn      = {978-3-8007-4977-5},
  url       = {https://nbn-resolving.org/urn:nbn:de:bsz:fn1-opus4-61352},
  pages     = {56 -- 60},
  year      = {2019},
  abstract  = {Cylindrical grinding is an important process in the manufacturing industry. During this process, the problem of grinding burn may appear, which can cause the workpiece to be worthless. In this work, a machine learning neural network approach is used to predict grinding burn based on the process parameters to prevent damage. A small dataset of 21 samples was gathered at a specific machine, grinding always the same element type with different process parameters. Each workpiece got a label from 0 to 3 after the process, indicating the severity of grinding burn. To get a robust neural network model, the dataset has been scaled by augmentation controlled by grinding experts, to generate more samples for training a neural network model. As a result, the model is able to predict the severity of grinding burn in a multiclass classification and it turned out that even with little data, the model performed well.},
  language  = {en}
}