Schmucker, B., Wang, C.-P., Zaeh, M. F., & Erkorkmaz, K. (2023). Wide-bandwidth cutting force monitoring via motor current and accelerometer signals CIRP Annals, 72 (in-press).
References
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Shirvani, H. K., Zeng, J. Q. C., & Erkorkmaz, K. (2022). Dynamic compliance attenuation in ball screw drives through model-based active damping of multiple vibration modes CIRP Annals, 71, 373-376. https://doi.org/https://doi.org/10.1016/j.cirp.2022.04.040
Franco, O., Beudaert, X., Erkorkmaz, K., & Munoa, J. (2022). Influence of guideway friction on the cutting point receptance in machine tools CIRP Annals, 71, 361-364. https://doi.org/10.1016/j.cirp.2022.04.045
Shirvani, H. K., Zeng, J. Q. C., Bevers, P., Oomen, T., & Erkorkmaz, K. (2022). Linear Time-Invariant Model Identification Algorithm for Mechatronic Systems Based on MIMO Frequency Response Data IEEE/ASME/Transactions/on/Mechatronics, 28, 703-714. https://doi.org/10.1109/TMECH.2022.3207116
Franco, O., Beudaert, X., Iglesias, A., Dombovari, Z., Erkorkmaz, K., & Munoa, J. (2022). Optimal cutting condition selection for high quality receptance measurements by sweep milling force excitation International Journal of Machine Tools and Manufacture, 176, 103873. https://doi.org/10.1016/j.ijmachtools.2022.103873
Shirvani, H. K., Hosseinkhani, Y., & Erkorkmaz, K. (2021). Suppression of harmonic positioning errors in ball-screw drives using Adaptive Feedforward Cancellation Precision Engineering, 68, 235-255. https://doi.org/https://doi.org/10.1016/j.precisioneng.2020.11.010
Azvar, M., Katz, A., Van Dorp, J., & Erkorkmaz, K. (2021). Chip geometry and cutting force prediction in gear hobbing CIRP Annals, 70, 95-98. https://doi.org/https://doi.org/10.1016/j.cirp.2021.04.082
Wang, C.-P., Erkorkmaz, K., McPhee, J., & Engin, S. (2020). In-process digital twin estimation for high-performance machine tools with coupled multibody dynamics CIRP Annals, 69, 321-324. https://doi.org/https://doi.org/10.1016/j.cirp.2020.04.047
Wong, D., Erkorkmaz, K., & Ren, C. L. (2020). RoboDrop: A Multi-Input Multi-Output Control System for On-Demand Manipulation of Microfluidic Droplets Based on Computer Vision Feedback IEEE/ASME/Transactions/on/Mechatronics, 25, 1129-1137. https://doi.org/https://doi.org/10.1109/TMECH.2020.2967999
Naiel, M. A., Ertay, D. S., Vlasea, M., & Fiegutha, P. (2020). Adaptive vision-based detection of laser-material interaction for directed energy deposition Additive Manufacturing, 36, 101468. https://doi.org/https://doi.org/10.1016/j.addma.2020.101468