eesti teaduste
akadeemia kirjastus
SINCE 1952
Proceeding cover
of the estonian academy of sciences
ISSN 1736-7530 (Electronic)
ISSN 1736-6046 (Print)
Impact Factor (2022): 0.9
Development of cyber-physical production systems based on modelling Technologies; pp. 348–355

Kashif Mahmood, Tatjana Karaulova, Tauno Otto, Eduard Shevtshenko

Internet of Things (IoT) is extending quite rapidly into the physical and virtual manufacturing worlds. IoT facilitates digitalization of manufacturing systems, which are considered vitally important to enhance the effectiveness and efficiency in the future manufacturing era. In order to compete globally and to meet the rapid market changes, manufacturing companies should consider implementation of manufacturing systems that are self-organized and decreasing constant human intervention to a minimum, still keeping the process under human control. In this paper, authors used the concept of Industry 4.0 to upgrade the manufacturing system according to the modern manufacturing needs. Manufacturing systems of Industry 4.0 are called cyber-physical production systems (CPPSs). On the other hand, modelling technologies such as process and data modelling, help to establish and understand the performance of a production system, as the evaluation of the control of an automated production system helps to make it reconfigurable and leads to grasping the idea behind the control mechanism of a CPPS. The aim of the paper is to develop a CPPS based on modelling technologies and to propose a concept of upgrading a relatively traditional production system.


    1.  Paavel, M., Karjust, K., and Majak, J. Development of a product lifecycle management model based on the fuzzy analytic hierarchy process. Proc. Est. Acad. Sci., 2017, 66(3), 279−286.

    2.  Monostori, L. Cyber-physical production systems: roots, expectations and R&D challenges. Procedia CIRP, 2014, 17, 9−13.

    3.  Lee, J., Bagheri, B., and Kao, H. A. A Cyber-Physical Systems architecture for Industry 4.0-based manu­facturing systems. Manuf. Lett., 2015, 3, 18−23.

    4.  Cellier, F. E. and Greifender, J. Continuous System Modeling. Int. Edition, Springer Science+ Business Media, New York,1991.

    5.  Soshnikov, D. and Dubovik, S. Knowledge-based busi­ness process modeling and simulation. In Proceedings of the 6th International Workshop on Computer Science and Information Technologies CSIT, Budapest, Hungary, 2004, 169−176.

    6.  Kaganski, S., Majak, J., Karjust, K., and Toompalu, S. Implementation of key performance indicators selection model as part of the Enterprise Analysis Model. Procedia CIRP, 2017, 63, 283−288.

    7.  Mahmood, K., Karaulova, T., Otto, T., and Shevtshenko, E. Performance Analysis of a Flexible Manufacturing System (FMS). Procedia CIRP, 2017, 63, 424−429.

    8.  Groover, M. P. Automation, Production Systems, and Computer-Integrated Manufacturing, 3rd ed. Pearson, London, 2015.

    9.  Noble, D. F. Forces of Production: A Social History of Industrial Automation. Transaction Publishers, New York, 2011.

 10.  Tolio, T., Ceglarek, D., ElMaraghy, H. A., Fischer, A., Hu, S. J., Laperričre, L. et al., SPECIES-Co-evolution of products, processes and production systems. CIRP Ann.-Manuf. Technol., 2010, 59(2), 672−693.

 11.  Wu, D., Rosen, D. W., Wang, L., and Schaefer, D. Cloud-based design and manufacturing: A new paradigm in digital manufacturing and design innovation. Comput.-Aided Des., 2015, 59, 1−14.

 12.  Bi, Z., Xu, L. D., and Wang, C. Internet of Things for enterprise systems of modern manufacturing. IEEE Trans. Ind. Inf., 2014, 10, 1537−1546.

 13.  Mourtzis, D., Vlachou, E., and Milas, N. Industrial Big Data as a result of IoT adoption in manufacturing. Procedia CIRP, 2015, 55, 290−295.

 14.  National Institute of Standards and Technology (NIST), 2014. (accessed 2017-03-20).

 15.  Chan, S., Lu, Y, and Wang, Y. Data-driven cost estimation for additive manufacturing in cyber manufacturing. J. Manuf. Syst., 2018, 46, 115−126.

 16.  Lee, E. A. and Seshia, S. A. Introduction to Embedded Systems. A Cyber-Physical Systems Approach, 2nd Edition. MIT Press, London, 2017.

 17.  Kuts, V., Otto, T., Tähemaa, T., Bukhari, K., and Pataraia, T. Adaptive industrial robots using machine vision. In Proceedings of the ASME 2018 International Mechanical Engineering Congress and Exposition, Pittsburgh, November 9–15, 2018, 1–8.

  18.  Pilz Australia Industrial Automation LP, The future of control systems and Industry 4.0. http: // (accessed 2016-03-01).


Back to Issue