Professor of School of Engineering, Design and Built Environment, Western Sydney University, Australia. His research interests cover Industry 4.0, Additive Manufacturing, Advanced Engineering Materials and Structures (Metals and Composites), Multi-scale Modelling of Materials and Structures, Metal Forming and Metal Surface Treatment.
Abstract—Kinematic redundant systems as part of machine tools reduce the dynamic requirements for feed axes and aim to increase the productivity. Yet, optimization of the system dynamic behaviour demands a deep understanding of how the dynamic coupling between the axes influences the tracking accuracy at the tool center point. This can be achieved through minimizing the discrepancies between the model output and physical measurements. One way is by optimizing the values of the dynamic coupling model parameters. In the present research, a heuristic algorithm, inspired by sailfish optimization algorithm, is developed to identify the stiffness and damping parameters of the investigated dynamic coupling model. Minimum RMS error is selected as the objective function parameter. Tests are conducted using different step and rectangular functions. Simulation results demonstrate the effectiveness of the proposed method to improve the model accuracy in simulating the vibrational response of kinematic redundant axes to jerk forces.
Index Terms—kinematic redundancy, feed drive systems, sailfish optimization algorithm, jerk induced vibrations, dynamic coupling
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