Concept of Developing a Gear Selection Tool for Improved Accuracy in Industrial Robotics
 
More details
Hide details
1
IIoT Controls and Technical Cybernetics, Fraunhofer IWU, Germany
 
 
Submission date: 2025-02-10
 
 
Final revision date: 2025-04-24
 
 
Acceptance date: 2025-04-24
 
 
Online publication date: 2025-05-26
 
 
Corresponding author
Muhammad Faisal Yaqoob   

IIoT Controls and Technical Cybernetics, Fraunhofer IWU, Germany
 
 
 
KEYWORDS
TOPICS
ABSTRACT
The application of robotics has evolved significantly through every industry. Robots do provide a wide range of motion, however their advantage of having lightweight components also limit the rigidity of the tool center point. Compensatory techniques involving joint stiffness determination and model-based predictions is one potential approach while another modern solution is the usage of precision gears. Higher rigidity and lower backlash found in precision gears as compared to conventional gears enable increased accuracy when carrying out production processes with industrial robots. A study at Fraunhofer IWU confirmed this by examining the impact of precision gear on a six-axis robot's accuracy during a milling process. Replacing all gears with precision gear technology or building new robots with them will certainly increase process accuracy. However, with over a half million robots already installed worldwide, there is a definite need to streamline the gear selection while enhancing the accuracy of existing robots with minimal effort and cost. This paper presents a proof of concept to develop a gear selection tool which utilizes robot’s MBS (multibody simulation) model involving gear parameters and process requirements to simplify gear selection for industrial processes. This tool aims to address the question “Which gear(s) needs to be replaced/installed in a robot to achieve the required/improved movement accuracy for an existing or new process?”
REFERENCES (18)
1.
UHLMANN E., 2023, Recent Advances in Precision, Sustainability and Safety of Machine Tools, Journal of Machine Engineering, 23/3, 58–68, https://doi.org/10.36897/jme/1....
 
2.
UHLMANN E., POLTE M., BLUMBERG J., LI Z., KRAFT A., 2021, Hyperparameter Optimization of Artificial Neural Networks to Improve the Positional Accuracy of Industrial Robots, Journal of Machine Engineering, 21/2, 47–59, https://doi.org/10.36897/jme/1....
 
3.
SCHNEIDER U., et al., 2016, Improving Robotic Machining Accuracy Through Experimental Error Investigation and Modular Compensation, Int. J Adv Manuf. Technol., 85, 1–4, 3–15, https://doi.org/10.1007/s00170....
 
4.
ZHU Z., et al., 2022, High Precision and Efficiency Robotic Milling of Complex Parts: Challenges, Approaches and Trends, Chinese Journal of Aeronautics, 35/2, 22–46, https://doi.org/10.1016/j.cja.....
 
5.
MAKULAVICIUS M., PETKEVICIUS S., ROZENE J., DZEDZICKIS A., BUCINSKAS V., 2023, Industrial Robots in Mechanical Machining: Perspectives and Limitations, Robotics, 12/6, 160, https://doi.org/10.3390/ robotics12060160.
 
6.
YE C., YANG J., DING H., 2022, High-Accuracy Prediction and Compensation of Industrial Robot Stiffness Deformation, International Journal of Mechanical Sciences, 233, 107638, https://doi.org/10.1016/j.ijme.... 2022.107638.
 
7.
CLEMENTS A., MULLINS R., Improve the Productivity of Robotics & Automation Systems with Lightweight Gears and Integrated Actuators - Whitepaper, available: www.harmonicdrive.net.
 
8.
OBERNEDER F., LANDLER S., OTTO M., VOGEL-HEUSER B., ZIMMERMANN M., STAHL K., 2024, Influences of Different Parameters on Selected Properties of Gears for Robot-Like Systems, Frontiers in robotics and AI, 11, 1414238, https://doi.org/10.3389/frobt.....
 
9.
CONCLI F., 2017, Low-Loss Gears Precision Planetary Gearboxes: Reduction of the Load Dependent Power Losses and Efficiency Estimation Through a Hybrid Analytical-Numerical Optimization Tool, Forsch Ingenieurwes, 81/4, 395–407, https://doi.org/10.1007/s10010....
 
10.
VERL A., VALENTE A., MELKOTE S., BRECHER C., OZTURK E., TUNC L.T., 2019, Robots in Machining, CIRP Annals, 68/2, 799–822, https://doi.org/10.1016/j.cirp....
 
11.
BUTUNOI P.A., STAN G., CIOFU C., UNGUREANU A.L., 2016, Research Regarding Backlash Improvement for Planetary Speed Reducers Used in the Actuation of Industrial Robots, AMM, 834, 114–119, https://doi.org/10.4028/www.sc....
 
12.
GIOVANNITTI E., NABAVI S., SQUILLERO G., TONDA A., 2022, A Virtual Sensor For Backlash in Robotic Manipulators, J. Intell. Manuf., 33/7, 1921–1937, 2022, https://doi.org/10.1007/s10845....
 
13.
SUN L., FANG L., 2017, Research on a Novel Robotic Arm with Non- Backlash Driving for Industrial Applications, IEEE 8th International Conference on CIS & RAM, Ningbo, China.
 
14.
NABTESCO, Getriebeauslegung bei 6-Achs-Robotern -Whitepaper Nabtesco: Whitepaper Roboterkonstruktion, [Online]. Available: info@nabtesco.de.
 
15.
ICH Motion, Precision Gearboxes Vs. Standard Gearboxes: Key Differences and Advantages, [Online], Available: https://www.ichmotion.com/Prec....
 
16.
MARWITZ J.A., et al., 2022, Accuracy Assessment of Articulated Industrial Robots Using the Extended- and the Loaded-Double-Ball-Bar, Journal of Machine Engineering, 22/2, 80–98, https://doi.org/10.36897/jme/ 149413.
 
17.
HÄNEL A., et al., 2021, Digital Twins for High-Tech Machining Applications—a Model-Based Analytics-Ready Approach, JMMP, 5/3, 80, https://doi.org/10.3390/jmmp50....
 
18.
GIRARDEAU-MONTAUT D., Cloud Compare 3D Point Cloud and Mesh Processing Software Open Source Project. [Online], https://www.danielgm.net/cc/.
 
eISSN:2391-8071
ISSN:1895-7595
Journals System - logo
Scroll to top