Automation of Optimization Synthesis for Modular Technological Equipment | Journal of Engineering Sciences

Automation of Optimization Synthesis for Modular Technological Equipment

Author(s): Zaleta O. M.1*, Povstyanoy O. Yu.1, Ribeiro L. F.2, Redko R. G.1, Bozhko T. Ye.1, Chetverzhuk T. I.1

Affiliation(s):
1 Lutsk National Technical University, 75, Lvivska St., 43018 Lutsk, Ukraine;
2 Instituto Politécnico de Bragança, 253, Alameda de Santa Apolónia, 5300-252 Bragança, Portugal

*Corresponding Author’s Address: [email protected]

Issue: Volume 10, Issue 1 (2023)

Dates:
Submitted: March 21, 2023
Received in revised form: May 12, 2023
Accepted for publication: May 19, 2023
Available online: May 22, 2023

Citation:
Zaleta O. M., Povstyanoy O. Yu., Ribeiro L. F., Redko R. G., Bozhko T. Ye., Chetverzhuk T. I. (2023). Automation of optimization synthesis for modular technological equipment. Journal of Engineering Sciences, Vol. 10(1), pp. A6-A14, doi: 10.21272/jes.2023.10(1).a2

DOI: 10.21272/jes.2023.10(1).a2

Research Area:  MANUFACTURING ENGINEERING: Machines and Tools

Abstract. Technological equipment design based on functionally modular methods is widely used in various technical fields. The designed object can be a technological machine, a production line, or a manufacturing complex. Special attention is paid to the optimization of its structure. The sequence of performing all stages of the optimization synthesis problem is presented in the article. To find a solution to this task, the developer should apply the complete or directed search of acceptable structure options and determine the best one using some optimization criteria to evaluate their quality. It can be simple enough if the designed technical system structure consists of no more than several elements. For example, if the number of alternative elements options is several dozen, it takes much time to accomplish the search correctly. Thus, the greater the number of components considered, the more difficult it is to do all the necessary calculations manually. In this case, machine resources should be involved. This scientific work aims to identify procedures of optimization synthesis that can be automated. Also, appropriate software has to be developed. Our computer program is based on the algorithm of a complete search of all options of the technical system structure. It can process an extensive array of input data and produce all possible and logically permissible results in the form the designer can analyze using the Pareto method to choose the best one. This software can be used for any technical system with a modular structure.

Keywords: equipment structure, optimization problem, software, industrialization, innovation, productivity.

References:

  1. Zabolotnyi, O., Zaleta, O., Bozhko, T., Chetverzhuk, T., Machado, J. (2022). Algorithmization of Functional-Modular Design of Packaging Equipment Using the Optimization Synthesis Principles. In:Innovations in Mechatronics Engineering II. ICIENG 2022. Lecture Notes in Mechanical Engineering. Springer, Cham, https://doi.org/10.1007/978-3-031-09385-2_13
  2. Shaik, A.M., Rao, V.V.S.K., Rao, C.S. (2015). Development of modular manufacturing systems – A review. Int J Adv Manuf Technol, Vol. 76, pp. 789–802 (2015), https://doi.org/10.1007/s00170-014-6289-2
  3. Yakovenko, I., Permyakov, A., Prihodko, O., Basova, Y., Ivanova, M. (2020). Structural Optimization of Technological Layout of Modular Machine Tools. In: Advanced Manufacturing Processes. InterPartner 2019. Lecture Notes in Mechanical Engineering. Springer, Cham, https://doi.org/10.1007/978-3-030-40724-7_36
  4. Yakovenko, I., Permyakov, A., Ivanova, M., Basova, Y., Shepeliev, D. (2022). Lifecycle Management of Modular Machine Tools. In: Tonkonogyi, V., Ivanov, V., Trojanowska, J., Oborskyi, G., Pavlenko, I. (eds) Advanced Manufacturing Processes III. InterPartner 2021. Lecture Notes in Mechanical Engineering. Springer, Cham, https://doi.org/10.1007/978-3-030-91327-4_13
  5. Uhlmann, E., Saoji, M., Peukert, B. (2016). Principles for interconnection of modular machine tool frames. Procedia CIRP, Vol. 40, pp. 413-418, https://doi.org/10.1016/j.procir.2016.01.081
  6. Peukert, B., Saoji, M., Uhlmann, E. (2015). An evaluation of building sets designed for modular machine tool structures to support sustainable manufacturing. Procedia CIRP, Vol. 26, pp. 612–617, https://doi.org/10.1016/j.procir.2014.07.175
  7. Yakovenko. I., Permyakov, A., Naboka, O., Prihodko, O., Havryliuk, Y. (2020). Parametric Optimization of Technological Layout of Modular Machine Tools. In: Ivanov V., Trojanowska J., Pavlenko I., Zajac J., Peraković D. (eds) Advances in Design, Simulation and Manufacturing III. DSMIE 2020. Lecture Notes in Mechanical Engineering. Springer, Cham, https://doi.org/10.1007/978-3-030-50794-7_9
  8. Usubamatov, R., Alwaise, A.M.A., Zain, Z.M. (2013). Productivity and optimization of section-based automated lines of parallel-serial structure with embedded buffers. Int J Adv Manuf Technol, Vol. 65, pp. 651–655, https://doi.org/10.1007/s00170-012-4204-2
  9. Pavlov, K.S., Khobotov, E.N. (2015). Models for equipment selection and upgrade in manufacturing systems of machine building enterprises. Autom Remote Control, Vol. 76, pp. 292–303, https://doi.org/10.1134/S0005117915020083
  10. Marmion, M.E. (2013). Local search and combinatorial optimization: from structural analysis of a problem to efficient algorithms design. 4OR-Q J Oper Res, Vol. 11, pp. 99–100, https://doi.org/10.1007/s10288-012-0204-1
  11. Guo, X., Cheng, G.D. (2010). Recent development in structural design and optimization. Acta Mech Sin, Vol. 26, pp. 807–823, https://doi.org/10.1007/s10409-010-0395-7
  12. Saliba, M.A., Azzopardi, S., Pace, C. et al. (2019). A heuristic approach to module synthesis in the design of reconfigurable manufacturing systems. Int J Adv Manuf Technol, Vol. 102, pp. 4337–4359, https://doi.org/10.1007/s00170-019-03444-4
  13. Kamrani, A.K., Gonzalez, R. (2003). A genetic algorithm-based solution methodology for modular design. Journal of Intelligent Manufacturing, Vol. 14, pp. 599–616, https://doi.org/10.1023/A:1027362822727
  14. Allen-Zhu, Z., Li, Y., Singh, A. et al. (2021). Near-optimal discrete optimization for experimental design: a regret minimization approach. Math. Program., Vol. 186, pp. 439–478, https://doi.org/10.1007/s10107-019-01464-2
  15. Wang, K., Zhou, Y., Tian, G. et al. (2021). A structured solution framework for fuzzy minimum spanning tree problem and its variants under different criteria. Fuzzy Optim Decis Making, Vol. 20, pp. 497–528, https://doi.org/10.1007/s10700-021-09352-1
  16. Chetverzhuk, T., Zabolotnyi, O., Sychuk, V., Polinkevych, R., Tkachuk, A. (2019). A method of body parts force displacements calculation of metal-cutting machine tools using CAD and CAE technologies. Annals of Emerging Technologies in Computing, Vol. 3(4), pp. 37–47, https://doi.org/10.33166/AETiC.2019.04.004
  17. Wen, X., Liu, J., Du, C. et al. (2022). The key technologies of machining process design: a review. Int J Adv Manuf Technol, Vol. 120, pp. 2903–2921, https://doi.org/10.1007/s00170-022-08982-y
  18. Kudryavtsev, Y.M. (2018). Structurally-parametrical optimization technological process by Dijkstra’s method in system Mathcad. Materials Science Forum. Vol. 931, pp. 1238–1244, https://doi.org/10.4028/www.scientific.net/msf.931.1238
  19. Calusdian, J., Yun, X. (2019). A simple and highly portable MATLAB interface for learning robotics. SN Appl. Sci., Vol. 1, 890, https://doi.org/10.1007/s42452-019-0941-2
  20. Xu, T., Chen, Z., Li, J. et al. (2015). Automatic tool path generation from structuralized machining process integrated with CAD/CAPP/CAM system. Int J Adv Manuf Technol, Vol. 80, pp. 1097–1111, https://doi.org/10.1007/s00170-015-7067-5
  21. Saavedra Sueldo, C., Perez Colo, I., De Paula, M. et al. (2023). ROS-based architecture for fast digital twin development of smart manufacturing robotized systems. Ann Oper Res, Vol. 322, pp. 75–99, https://doi.org/10.1007/s10479-022-04759-4
  22. Lan, H., Ding, Y., Hong, J. et al. (2008). A re-configurable cross-sectional imaging system for reverse engineering based on a CNC milling machine. Int J Adv Manuf Technol, Vol. 37, pp. 341–353, https://doi.org/10.1007/s00170-007-0962-7
  23. Krimpenis, A.A., Fountas, N.A., Ntalianis, I. et al. (2014). CNC micromilling properties and optimization using genetic algorithms. Int J Adv Manuf Technol, Vol. 70, pp. 157–171, https://doi.org/10.1007/s00170-013-5248-7
  24. Obertyukh, R., SlabkyіA., Polishchuk, L., Povstianoi, O., Kumargazhanova, S., Satymbekov, M. (2022). Dynamic and mathematical models of the hydroimpulsive vibro-cutting device with a pressure pulse generator bult into the ring spring. Informatyka, Automatyka, Pomiary w Gospodarce i Ochronie Środowiska, Vol. 12(3), pp. 54–58, https://doi.org/10.35784/iapgos.3049
  25. Povstyanoy, O., Zabolotnyi, O., Kovalchuk, O., Somov, D., Chetverzhuk, T., Gromaszek, K., Amirgaliyeva, S., Denissova, N. (2021). Analysis, Development, and Modeling of New Automation System for Production of Permeable Materials from Machining Waste. Mechatronic Systems, Vol. 1. Taylor & Francis Group, London, UK, https://doi.org/10.1201/9781003224136-14
  26. Nikitchenko, N.S. (2003). Equitone predicate algebras and their applications. Cybernetics and Systems Analysis, Vol. 39, pp. 97–112, https://doi.org/10.1023/A:1023829327704

Full Text



© 2014-2024 Sumy State University
"Journal of Engineering Sciences"
ISSN 2312-2498 (Print), ISSN 2414-9381 (Online).
All rights are reserved by SumDU