Digital modeling and AI-driven optimization of ball burnishing process parameters
Diyan M. DIMITROV, Stoyan SLAVOV, Lyubomir Si Bao VAN
Abstract. In this paper, a digital model of the ball burnishing process, developed using data from tests on bronze and aluminum alloys, to create regular micro-reliefs on a cylindrical surface.” is presented. The experiments were carried out on a CNC lathe using a cylindrical stock. The input features were burnishing force, feedrate, machine condition and material type, while the results were surface roughness and hardness. A Bayesian approach was used to infer the parameters of the digital model, enabling the creation of distributions that directly account for uncertainty caused to regression coefficients and by random factors incorporated in machine condition feature. The proposed digital model was subsequently used to run simulations and check various optimization algorithms. Additionally, a large language model NexusRaven-V2 that were fine tunned for function calling is combined with the functions from the digital model.
Keywords
Ball Burnishing, Surface Hardness, Surface Roughness, Bayesian Approach, Digital Model, Optimization, LLM, Function Calling
Published online 12/10/2024, 8 pages
Copyright © 2024 by the author(s)
Published under license by Materials Research Forum LLC., Millersville PA, USA
Citation: Diyan M. DIMITROV, Stoyan SLAVOV, Lyubomir Si Bao VAN, Digital modeling and AI-driven optimization of ball burnishing process parameters, Materials Research Proceedings, Vol. 46, pp 354-361, 2024
DOI: https://doi.org/10.21741/9781644903377-46
The article was published as article 46 of the book Innovative Manufacturing Engineering and Energy
Content from this work may be used under the terms of the Creative Commons Attribution 3.0 license. Any further distribution of this work must maintain attribution to the author(s) and the title of the work, journal citation and DOI.
References
[1] S.D. Slavov, D.M. Dimitrov, Modelling the dependence between regular reliefs ridges height and the burnishing regime’s parameters for 2024 aluminum alloy processed by using CNC-lathe machine, IOP Conference Series: Materials Science and Engineering, 1037 (2021) 012016. https://doi.org/10.1088/1757-899X/1037/1/012016
[2] S.D. Slavov, D.M. Dimitrov, M.I. Konsulova-Bakalova, Advances in burnishing technology, in: Advanced Machining and Finishing, Elsevier, Amsterdam, The Netherlands, 2021, pp. 481-525. https://doi.org/10.1016/B978-0-12-817452-4.00002-6
[3] G. Nagit, O. Dodun, L. Slatineanu, M. Ripanu, A. Mihalache, A. Hrituc, Influence of some process input factors on the main dimensions of the grooves generated during vibroburnishing, IOP Conference Series: Materials Science and Engineering, 968 (2020) 012007. https://doi.org/10.1088/1757-899X/968/1/012007
[4] Yu.G. Schneider, Operational properties of parts with regular microrelief, IVA Publishing, St. Petersburg, 2001. ISBN 5-7577-0166-8.
[5] S.S. Pande, S.M. Patel, Investigations on vibratory burnishing process, Int. J. Mach. Tool Des. Res. 24 (1984) 195-206. https://doi.org/10.1016/0020-7357(84)90004-0
[6] V. Dzyura, P. Maruschak, O. Prentkovskis, Determining optimal parameters of regular microrelief formed on the end surfaces of rotary bodies, Algorithms 14 (2021) 46. https://doi.org/10.3390/a14020046
[7] E.M. Frazzon, A. Albrecht, P.A. Hurtado, Simulation-based optimization for the integrated scheduling of production and logistic systems, IFAC-PapersOnLine 49 (2016) 1050-1055. https://doi.org/10.1016/j.ifacol.2016.07.581
[8] M. Kritzinger, G. Karner, J. Traar, J. Henjes, W. Sihn, Digital Twin in manufacturing: A categorical literature review and classification, IFAC-PapersOnLine 51 (2018) 1016-1022. https://doi.org/10.1016/j.ifacol.2018.08.474
[9] E. Velázquez‐Corral, J. Llumà, R. Jerez‐Mesa, V. Wagner, G. Dessein, J.A. Travieso‐Rodriguez, Fatigue enhancement and hardening effect through ultrasonic vibration‐assisted ball‐burnishing process on AISI 1045 steel, Fatigue Fract. Eng. Mater. Struct. 47 (2024) 203-219. https://doi.org/10.1111/ffe.14180
[10] C. Felhő, F. Tesfom, G. Varga, ANOVA Analysis and L9 Taguchi Design for Examination of Flat Slide Burnishing of Unalloyed Structural Carbon Steel, J. Manuf. Mater. Process. 7 (2023) 136. https://doi.org/10.3390/jmmp7040136
[11] H. Yamazaki, J. Zhu, T. Tanaka, Study on the Surface Enhancement of Thin-Walled Metallic Materials Using a Novel Double-Side Burnishing Tool, Int. J. Autom. Technol. 17 (2023) 458-468. https://doi.org/10.20965/ijat.2023.p0458
[12] P. Pawlus, W. Koszela, R. Reizer, Surface texturing of cylinder liners: A review, Materials 15 (2022) 8629. https://doi.org/10.3390/ma15238629
[13] T. Dovramadjiev, D. Dobreva,T. Murzova,M. Murzova, V. Markov, I. Iliev, K. Cankova, G. Jecheva, G. Staneva, Interaction Between Artificial Intelligence, 2D and 3D Open Source Software, and Additive Technologies for the Needs of Design Practice, InWorld Conference on Information Systems for Business Management vol.834 pp. 339-350 (2023). Singapore: Springer Nature Singapore. https://doi.org/10.1007/978-981-99-8349-0_26
[14] Z. Liu, T. Hoang, J. Zhang, M. Zhu, T. Lan, S. Kokane, J. Tan, W. Yao, Z. Liu, Y. Feng, R. Murthy, Apigen: Automated pipeline for generating verifiable and diverse function-calling datasets, arXiv preprint arXiv:2406.18518, 2024.
[15] V.K. Srinivasan, Z. Dong, B. Zhu, B. Yu, D. Mosk-Aoyama, K. Keutzer, J. Jiao, J. Zhang, Nexusraven: A commercially-permissive language model for function calling, in: NeurIPS 2023 Foundation Models for Decision Making Workshop, 2023.
