Home > Published Issues > 2022 > Volume 11, No. 3, March 2022 >
IJMERR 2022 Vol.11(3): 187-191

Model Optimization of Kinematic Redundant Feed Drive Systems Using Sailfish Optimization Algorithm

M. G. A. Nassef1, Taha Mostafa2, and Christian Schenck3
1. Industrial and Manufacturing Engineering Department, Egypt-Japan University of Science and Technology, Alexandria, Egypt
2. Mechanical Engineering Department, Alexandria University, Alexandria, Egypt
3. Bremen institute for mechanical Engineering, University of Bremen, Bremen, Germany

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

Cite: M. G. A. Nassef, Taha Mostafa, and Christian Schenck, "Model Optimization of Kinematic Redundant Feed Drive Systems Using Sailfish Optimization Algorithm," International Journal of Mechanical Engineering and Robotics Research, Vol. 11, No. 3, pp. 187-191, March 2022. DOI: 10.18178/ijmerr.11.3.187-191

Copyright © 2022 by the authors. This is an open access article distributed under the Creative Commons Attribution License (CC BY-NC-ND 4.0), which permits use, distribution and reproduction in any medium, provided that the article is properly cited, the use is non-commercial and no modifications or adaptations are made.