Optimizing cutting fluid usage in cutting processes on CNC machines: A conceptual digital twin model for ecological sustainability





Cutting fluid optimization, Digital twin, SADT (Structured Analysis and Design Technique), Ecological sustainability, Sensors system


The increasing demand for environmentally friendly manufacturing processes has led to the need for optimizing the use of cutting fluids in turning and milling processes. Cutting fluids are commonly used in cutting processes to reduce tool wear and improve cutting performance. However, cutting fluids have a negative impact on environment and human health. This paper proposes a conceptual model of аn information system based on digital twin of the production process. This system will enable monitoring of the manufacturing process and provide a decision support system for helping industrial engineers manage its parameters. The model is represented by using SADT (Structured Analysis and Design Technique), and it is presented by using one of the most common problems of optimizing cutting fluid usage in cutting processes on CNC machines from an ecological perspective. The proposed model considers various cutting process parameters (cutting speed, feed rate, depth of cut) and cutting environment factors (cutting process temperature) to determine the optimal cutting fluid flow rate. To optimize the usage of cutting fluid, the smart information system acquires, processes, and stores data from cutting process temperature and cutting fluid flow sensors to establish the correlation between process parameters and sensor data, which is then used to develop a model. The proposed model can be integrated with existing CNC machines to reduce environmental impact while maintaining high productivity. This paper provides a promising approach for optimizing cutting fluid usage in CNC machining processes while promoting ecological sustainability.


Download data is not yet available.


D. Fratila, Environmentally friendly manufacturing processes in the context of transition to sustainable production, Comprehensive Materials Processing 8 (2014) pp. 163-175. https://doi.org/10.1016/B978-0-08-096532-1.00815-3

M. M. Rahman, M. Hassan, M. Abdullah-Al Bari, An optimize system of using cutting fluid in machining operation for light work machine shop, International Journal of Engineering and Applied Sciences 4 (1) (2012) pp. 1-8. https://dergipark.org.tr/tr/download/article-file/217686

S. Debnath, M. M. Reddy, Q. S. Yi, Environmental friendly cutting fluids and cooling techniques in machining: a review, Journal of cleaner production 83 (2014) pp. 33-47. https://doi.org/10.1016/j.jclepro.2014.07.071

X. C. Tan, F. Liu et al., A decision-making framework model of cutting fluid selection for green manufacturing and a case study, Journal of Materials processing technology 129 (1-3) (2002) pp. 467-470. https://doi.org/10.1016/S0924-0136(02)00614-3

A. Alok, S. Kumar, et al., Review on the effect of surface textured tool in the field of machining, Advances in Materials and Processing Technologies (2023) pp. 1-19. https://doi.org/10.1080/2374068X.2023.2184580

H. J. Heine, Dry Machining: A Promising Option, Foundry management & technology 126 (9) (1998) pp. 50-56.

N. King, L. Keranen, et al., Wet versus dry turning: a comparison of machining costs, product quality, and aerosol formation, SAE Technical Paper (2001) 2001-01-0343. https://doi.org/10.4271/2001-01-0343

E. Benedicto, D. Carou, E. M. Rubio, Technical, economic and environmental review of the lubrication/cooling systems used in machining processes, Procedia engineering 184 (2017) pp. 99-116. https://doi.org/10.1016/j.proeng.2017.04.075

M. Barać, N. Vitković, M. Manić, Conceptual model of an information system for measuring cutting fluid temperature on CNC machines, in: J. Baralić, N. Dučić (Eds.), 38th International Conference of Production Engineering: ICPE-S 2021, University of Kragujevac, Faculty of Technical Sciences Čačak, Serbia, 2021, pp. 68-75. http://spms.fink.rs/doc/2021/Proceedings%20SPMS%202021.pdf

C. G. Yuan, A. Pramanik, et al., Drilling of titanium alloy (Ti6Al4V)–a review, Machining Science and Technology 25 (4) (2021) pp. 637-702. https://doi.org/10.1080/10910344.2021.1925295

T. H. C. Childs, K. Maekawa, P. Maulik, Effects of coolant on temperature distribution in metal machining, Materials science and technology, 4(11), (1988) pp. 1006-1019. https://doi.org/10.1179/mst.1988.4.11.1006

F. Wilking, B. Schleich, S. Wartzack, Digital twins-definitions, classes and business scenarios for different industry sectors, in: Proceedings of the International Conference on Engineering Design (ICED21), Cambridge University Press, Gothenburg, Sweden, 1, 2021, pp. 1293-1302. https://doi.org/10.1017/pds.2021.129

D. A. Marca, C. L. McGowan, SADT: Structured Analysis and Design Technique, McGraw-Hill Book Co., Inc.: New York, 1988.

X. Liu, D. Jiang, et al., A systematic review of digital twin about physical entities, virtual models, twin data, and applications, Advanced Engineering Informatics, 55 (2023) 101876.https://doi.org/10.1016/j.aei.2023.101876

W. Kritzinger, M. Karner, et al., Digital Twin in manufacturing: A categorical literature review and classification, in: M. Macchi, L. Monostori, R. Pinto (Eds.), 16th IFAC Symposium on Information Control Problems in Manufacturing INCOM 2018: IFAC (International Federation of Automatic Control) Proceedings Volume 51 (11), IFAC-PapersOnLine with Elsevier, Bergamo, Italy, 2018, pp. 1016-1022. https://doi.org/10.1016/j.ifacol.2018.08.474

C. Bianconi, A. Bonci, et al., (2020, System thinking approach for digital twin analysis, in: 2020 IEEE International Conference on Engineering, Technology and Innovation (ICE/ITMC), IEEE, Cardiff, UK, 2020, pp. 1-7. https://doi.org/10.1109/ICE%2FITMC49519.2020.9198392

D. T. Ross, K. E. Schoman, Structured Analysis for Requirements Definition, IEEE Transactions on Software Engineering, SE-3 (1) (1977) pp. 6-15. https://doi.org/10.1109/TSE.1977.229899

F. Ahmed, S. Robinson, A. A. Tako, Using the structured analysis and design technique (SADT) in simulation conceptual modelling, in: A. Tolk, S. D. Diallo, I. O. Ryzhov, L. Yilmaz, S. Buckley, J. A. Miller (Eds.), Proceedings of the 2014 winter simulation conference, New York: IEEE, Savannah, GA, USA, 2014, pp. 1038–1049. http://doi.org/10.1109/WSC.2014.7019963

M. E. Dickover, C. L. McGowan, D. T. Ross, Software design using: SADT, in: Proceedings of the 1977 annual conference (ACM '77). Association for Computing Machinery, New York, NY, USA, 1977, pp. 125–133. https://doi.org/10.1145/800179.810192

D. T. Ross, Applications and Extensions of SADT, Computer, 18 (4) (1985) pp. 25-34. https://doi.org/10.1109/MC.1985.1662862

M. Barać, N. Vitković et al., A review of machine learning methods applied in smart machining, in: M. Zdravković, M. Trajanović, Z. Konjović (Eds.), 12th International Conference on Information Society and Technology: ICIST 2022 Proceedings, Information Society of Serbia – ISOS, Belgrade, Serbia, 2022, pp. 244-246. https://www.eventiotic.com/eventiotic/files/Papers/URL/96770bc7-0fd1-442f-95f9-d2e805b598a2.pdf




How to Cite

Barać, M., Vitković, N., Marinković, D., Janković, P., & Mišić, D. (2023). Optimizing cutting fluid usage in cutting processes on CNC machines: A conceptual digital twin model for ecological sustainability. Acta Technica Jaurinensis, 16(3), 90–98. https://doi.org/10.14513/actatechjaur.00697



Research articles