@article{12,
  keywords = {Computer Numerical Control (CNC), Identification, Multibody dynamics},
  author = {Chia-Pei Wang and Kaan Erkorkmaz and John McPhee and Serafettin Engin},
  title = {In-process digital twin estimation for high-performance machine tools with coupled multibody dynamics},
  abstract = {<p>Direct drive rotary and linear actuators significantly enhance the performance of multi-axis machine tools. The absence of mechanical gearing, however, increases the nonlinear dynamic coupling between the axes, making it challenging to identify accurate virtual models or so-called ‘digital twins’. This article presents a new approach to estimate nonlinear multivariable dynamic models non-intrusively, using in-process CNC data. Major influences, such as multi-rigid body motion, actuator force/torque ripples, nonlinear friction, feedforward/feedback control, and vibration modes, are systematically detected and identified. The new method is demonstrated in digital twin estimation for a 5-axis laser drilling machine.</p>
},
  year = {2020},
  journal = {CIRP Annals},
  volume = {69},
  pages = {321-324},
  issn = {0007-8506},
  url = {https://www.sciencedirect.com/science/article/pii/S000785062030069X},
  doi = {https://doi.org/10.1016/j.cirp.2020.04.047},
}