@article{18,
  keywords = {CNC, Drive, Identification},
  author = {Ginette Tseng and Christina Chen and Kaan Erkorkmaz and Serafettin Engin},
  title = {Digital shadow identification from feed drive structures for virtual process planning},
  abstract = {<p>A requirement for Industry 4.0 style manufacturing is the ability to identify, update, and utilize ‘digital shadow’ type reduced order mathematical models of machine tools and processes, in a non-intrusive and effective manner. This paper presents two new approaches for building such CNC feed drive - structure models from in-process data, typically without interrupting the production. These models can be used to simulate and optimize multi-axis manufacturing trajectories, so that quality and cycle time reduction objectives can be met. The proposed methodology has been demonstrated experimentally on machine tools, and in the context of gas turbine engine component manufacture.</p>
},
  year = {2019},
  journal = {CIRP Journal of Manufacturing Science and Technology},
  volume = {24},
  pages = {55-65},
  issn = {1755-5817},
  url = {https://www.sciencedirect.com/science/article/pii/S1755581718300609},
  doi = {https://doi.org/10.1016/j.cirpj.2018.11.002},
}