OPTIMIZATION OF PREDICTIVE MODELS FOR THE THERMAL BEHAVIOR OF LEADING AND THREADED SPINDLES AND THEIR CORRESPONDING ROLLING BEARINGS USING AI AND BIG DATA

11th International Scientific Conference Research and Development of Mechanical Elements and Systems IRMES (2025) [pp. 133-138]  

AUTHOR(S) / AUTOR(I): Vladislav KRSTIĆ , Dragan MILČIĆ , Miodrag MILČIĆ

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DOI: 10.46793/IRMES25.133K

ABSTRACT / SAŽETAK:

The thermal behavior of mechanical components has a critical impact on the accuracy and reliability of machine tools, especially for components such as leading spindles, threaded spindles, and their rolling bearings. During intensive operation, heat is generated due to friction, internal losses in lubrication, and external influences, leading to thermal deformations, increased clearance, and loss of positioning.

Managing and predicting the thermal behavior of these components is crucial for ensuring machining accuracy, particularly in high-precision and CNC machine tools. The development of numerical methods such as thermal FEM simulations and the application of deep learning models and artificial intelligence have enabled a deeper understanding and better prediction of thermal effects. Additionally, a large number of new algorithms allow for the optimization of models, particularly in the domain of boundary conditions in thermal models.

The aim of this research is to present methods for optimizing models for predicting the thermal behavior of leading and threaded spindles and their bearings under real operating conditions, based on collected temperature data, operating parameters, and experimental validation.

KEYWORDS / KLJUČNE REČI:

boundary conditions; AI; Big Data; product optimization; Fourth industrial revolution

ACKNOWLEDGEMENT / PROJEKAT:

REFERENCES / LITERATURA:

  • G., Reinhart.: Handbuch Industrie 4.0- Geschäftsmodelle, Prozesse, Technik, Carl Hanser Verlag, ISBN: 978-3-446-44642-7, 2017
  • Bin, C., Xin, G., Decheng, C., Haolin, L: Simulation on thermal characteristics of high-speed motorized spindle. Case Studies in Thermal Engineering, Volume 35,  102144,  ISSN  2214-157X,  2022 https://doi.org/10.1016/j.csite.2022.102144
  • Jialan, L., Chi, M., Shilong, W., Sibao, W., Bo, Y., Hu, S.: Thermal-structure interaction characteristics of a high-speed spindle-bearing system. International   Journal of Machine Tools and Manufacture, Volume             137, ISSN 0890-6955, 2019 https://doi.org/10.1016/j.ijmachtools.2018.10.004.
  • Živković, A. M., Zeljković, M. V., Mlađenović, C. D., Tabaković, S. T., Milojević, Z. L., Hadžistević, M. J.: A study of thermal behavior of the machine tool spindle, Thermal Science, 23(3 Part B), 2117-2130. 2019 https://doi.org/10.2298/TSCI180129118Z
  • Zhou, C., Qu, Z., Hu, B., & Li, S.: Thermal network model and experimental validation for a motorized spindle including thermal–mechanical coupling effect, The International Journal of Advanced Manufacturing Technology, 115(1), pp. 487-501. 2021 https://doi.org/10.21203/rs.3.rs-159145/v1
  • Yang, Y., Du, Z., Feng, X., & Yang, J.: Real-time thermal modelling approach of a machine tool spindle based on bond graph method, The international     journal of advanced manufacturing technology, 113(1), pp.     99-115.  2021 https://doi.org/10.1007/s00170-021-06611-8
  • Ilić, G., Vukić, M., Radojković, N., Živković, P., Stojanović. : Termodinamika II – Osnove prostiranja toplote i materije, Mašinski fakultet Univerziteta u Nišu, ISBN 978-86-6055-056-1, 2014
  • Krstić., V.: Istraživanje konstrukciono-triboloških parametera kugličnih ležaja sa kosim dodirom tipa ZKLF sa aspekta optimalne osnovne funkcije – doktorska disertacija, Mašinski fakultet Univerziteta u Nišu 2018
  • https://automatika.elfak.ni.ac.rs/files/Nastavni_materijal/Softver%20za%20simulaciju/Skripta_Softver%20za%20simulaciju%20dinamickih%20sistema.pdf, Accessed on: 2025-04-15
  • Krstić, V., Milčić, D., Milčić, M.: Thermal Analysis of the Threaded Spindle Bearing Assembly in Numerically Controlled Machine Tools, Facta Universitatis, Series: Mechanical Engineering, ISSN 0354-2025. 16, 2, pp. 261-261. 2018, DOI: 10.22190/fume170512022k
  • Krstić, V. Milčić, D.: Numerical Analysis of the Thermal Load of the Bearing Assembly of Threaded Spindle Realized Using the ZKLN- and ZKLF-Type Bearing, Proceedings of The 3rd International Conference Mechanical Engineering in XXI Century, ISBN: 978-86-6055-072-1, 2015
  • Krstić, V., Milčić, D.: The Research of Heat Balance of Bearing Mounting Realized by Axial Ball Bearings with Angular Contact Intended For the Threaded Spindles, 17th Symposium on Thermal Science and Engineering of Serbia, Sokobanja., ISBN: 978-86-6055-076-9 , 2015
  • Zhang, L., Xuan, J., Shi, T.: Obtaining More Accurate Thermal Boundary Conditions of Machine Tool Spindle Using Response Surface Model Hybrid Artificial Bee Colony Algorithm, Symmetry, 12(3), 361, 2020, https://doi.org/10.3390/sym12030361
  • Liu, Y., C., Li, K..Y., Tsai, Y.C.: Spindle Thermal Error Prediction Based on LSTM Deep Learning for a CNC Machine Tool, Applied Sciences, 11(12), 5444.  2021 https://doi.org/10.3390/app11125444
  • Krstić, V., Milčić, D., Madić, M., Milčić, M., Milovančević, M.: Prediction of Friction Torque and Temperature on Axial Angular Contact Ball Bearings for Threaded Spindle Using Artificial Neural Network, Journal of Vibration Engineering & Technologies, ISSN: 2523-3920. 10, 4 pp 1473-2022, DOI: 10.1007/s42417-022-00461-8
  • Milčić D., Alsammarraie, A., Madić, M., Krstić, V., Milčić, M.: Predictions of Friction Coefficient in Hydrodynamic Journal Bearing Using Artificial Neural Networks, Strojniški vestnik – Journal of Mechanical Engineering, ISSN: 0039-2480. Vol. 67, 9 411-420, 202, DOI: 10.5545/sv-jme.2021.7230
  • Li, B., Tian, X., Zhang, M.: Thermal error modeling of machine tool spindle based on the improved algorithm optimized BP neural network, ).. Int J Adv Manuf Technol, 105, pp. 1497–1505, 2019, https://doi.org/10.1007/s00170-019-04375-w