This project focuses on improving the performance of industrial robotic arms in high-force machining operations such as milling and drilling, particularly in applications like aircraft fuselage assembly, large-scale manufacturing, and hybrid additive-subtractive processes. A key challenge in adopting robotic systems for these operations is the deflections and vibrations that occur during machining, which can negatively impact precision and efficiency. In this project, we leverage advanced modelling and process optimization techniques to minimize these vibrations, enhancing the accuracy and reliability of robotic machining processes. By addressing these challenges, we aim to unlock the full potential of robots in demanding industrial applications. Read more in the following articles:

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• Huynh, Hoai Nam, et al. "Modelling the dynamics of industrial robots for milling operations." Robotics and Computer-Integrated Manufacturing 61 (2020): 101852. https://doi.org/10.1016/j.rcim.2019.101852

• Mohammadi, Y., Ahmadi, K. In-Process Frequency Response Function Measurement for Robotic Milling. Exp Tech 47, 797–816 (2023). https://doi.org/10.1007/s40799-022-00590-5

• Chen, Han, and Keivan Ahmadi. "Estimating pose-dependent FRF in machining robots using multibody dynamics and Gaussian Process Regression." Robotics and Computer-Integrated Manufacturing 77 (2022): 102354. https://doi.org/10.1016/j.rcim.2022.102354

• Chen, Han, and Keivan Ahmadi. "Estimating pose-dependent FRF in machining robots using multibody dynamics and Gaussian Process Regression." Robotics and Computer-Integrated Manufacturing 77 (2022): 102354. https://doi.org/10.1016/j.ymssp.2021.108523

• Mohammadi, Yaser, and Keivan Ahmadi. "Single degree-of-freedom modeling of the nonlinear vibration response of a machining robot." Journal of Manufacturing Science and Engineering 143.5 (2021): 051003. https://doi.org/10.1115/1.4048513