Digital Twin: Industrial Robot Kinematic Model Integration to the Virtual Reality Environment
 
More details
Hide details
1
Tallinn University of Technology, School of Engineering, Department of Mechanical and Industrial Engineering, Tallinn, Estonia
 
 
Submission date: 2020-01-31
 
 
Acceptance date: 2020-03-24
 
 
Online publication date: 2020-06-24
 
 
Publication date: 2020-06-24
 
 
Journal of Machine Engineering 2020;20(2):53-64
 
KEYWORDS
ABSTRACT
Digital Twin (DT) concept nowadays is shown via the simulations of the manufacturing systems and included those production processes and parametric 3D models of the product. It is the primary method for planning, analysing and optimising the factory layout and processes. Moreover, work on management via the simulation in real-time is already done using Virtual Reality (VR) tools from a safe and remote environment. However, there is a list of limitation of such kind of digital systems, as connectivity speed and precision of the digital environment. The primary goal of this study is to access second listed limitation and on the example of the fully synchronised physical with its digital replica industrial robot, increase the level of precision of the developed DT environment. The proposed approach introduces transfer of the mathematical model to the virtual environment, thus creating a precise and scaled visual model of the Industrial Robot.
ACKNOWLEDGEMENTS
This research was supported by project AR16077 Smart Industry Centre (SmartIC) No. 2014-2020.4.01.16-0183, supported by the EU Regional Development Fund. The authors are grateful to the student of the Tallinn University of Technology – Yevhen Bondarenko, for his help in the experiments with Virtual Reality implementations and laboratory digitalisation.
 
REFERENCES (21)
1.
LU Y., 2017, Industry 4.0: A survey on technologies, applications and open research issues, Journal of Industrial Information Integration, 6, 1–10.
 
2.
GRIEVES M.W., 2015, Digital Twin: Manufacturing Excellence through Virtual Factory Replication, White Paper.
 
3.
TERKAJ W., TOLIO T., URGO M., 2015, A virtual factory approach for in situ simulation to sup-port production and maintenance planning, CIRP Annals – Manufacturing Technology, 451–454.
 
4.
TAO F., ZHANG M., 2017, About the importance of Autonomy and Digital Twins for the future of manufacturing, IEEE Access – Special Section on Key Technologies for Smart Factory of Industry 4.0, 20418–20427.
 
5.
KUTS V., OTTO T., TÄHEMAA T., BONDARENKO Y., 2019, Digital Twin based synchronised control and simulation of the industrial robotic cell using Virtual Reality, Journal of Machine Engineering, 19/1, 128–145.
 
6.
MAHMOOD K., SHEVTSHENKO E., 2015, Analysis of machine production processes by risk assessment approach, Journal of Machine Engineering, 15/1, 112−124.
 
7.
KUTS V., OTTO T., TÄHEMAA T., BUKHARI K., PATARAIA T., 2018, Adaptive industrial robots using machine vision, ASME International Mechanical Engineering Congress and Exposition, Pittsburgh, Pennsylvania, USA.
 
8.
SELL R., OTTO T., 2008, Remotely controlled multi robot environment, 19th EAEEIE Annual Conference, Tallinn.
 
9.
SELL R., 2013, Remote Laboratory Portal for Robotic and Embedded System Experiments, International Journal of Online Engineering, 9, 23−26.
 
10.
KUTS V., SARKANS M., OTTO T., TÄHEMAA T., 2017, Collaborative work between human and Industrial robot in manufacturing by advanced safety Monitoring System, Proceedings of the 28th DAAAM International Symposium, Vienna.
 
11.
KUTS V., TÄHEMAA T., OTTO T., SARKANS M., LEND H., 2016, Robot manipulator usage for measurement in production areas, Journal of Machine Engineering, 16/1, 57−67.
 
12.
Industrial Virtual and Augmented Reality Laboratory Homepage, http://ivar.ttu.ee/, last accessed 2020/01/27.
 
13.
ABB Robotics, 2018, Product specification IRB 1600/1660, Sweden.
 
14.
ASADA H.H., 2005, Introduction to Robotics, Lecture Notes. Massachusetts Institute of Technology.
 
15.
SHAH S.V., SAHA S.K., DUTT J.K., 2012, Denavit-Hartenberg Parameterization of Euler Angles, Journal of Computational and Nonlinear Dynamics, 7/2, 146–152.
 
16.
TZAFESTAS S.G., 2013, Introduction to Mobile Robot Control, 1st ed. Elsevier, Amsterdam.
 
17.
CRAIG J., 2005, Introduction to Robotics: Mechanics and Control, 3rd ed. Pearson Education International, New Jersey.
 
18.
MEGAHED S.M, 1992, Inverse kinematics of spherical wrist robot arms: Analysis and simulation, Journal of Intelligent and Robotic Systems, 5/3, 211–227.
 
19.
KUCUK S., BINGUL Z., 2004, The inverse kinematics solutions of industrial robot manipulators, Proceedings of the IEEE International Conference on Mechatronics, Istanbul, 274–279.
 
20.
ABB Robotics, 2017, Technical reference manual: RAPID Instructions, Functions and Data Types, Sweden.
 
21.
WILLMOTT C., MATSUURA K., 2005, Advantages of the Mean Absolute Error (MAE) over the Root Mean Square Error (RMSE) in assessing average model performance, Climate Research, 30, 79–82.
 
eISSN:2391-8071
ISSN:1895-7595
Journals System - logo
Scroll to top