Modares Mechanical Engineering

Modares Mechanical Engineering

Evaluation and Prediction of Surface Roughness and Microhardness in the Deep Ball Burnishing Process Using Machine Learning Approach

Document Type : Original Article

Authors
Javad Kazemi
Amir Rasti
Alireza Zarhoon
Faculty of Mechanical Engineering-Tarbiat Modares University
10.48311/mme.2026.118727.82926
Abstract
Surface quality of engineering components directly affects their performance and service life. Conventional machining processes, particularly under conditions that induce high surface roughness and tensile residual stresses, are often unable to ensure the desired final surface quality. Consequently, the application of surface enhancement techniques such as deep ball burnishing (DBB) is essential for improving surface integrity. In this study, the combined effects of key DBB process parameters, including ball diameter, penetration depth, and feed rate, on surface quality indices—Ra, Rz, and surface microhardness—were analyzed. Advanced machine learning models were employed for predictive modeling, with the SVR model demonstrating the best performance, achieving R² values of 0.925, 0.942, and 0.910 for Ra, Rz, and microhardness, respectively. Although the XGBoost model also produced acceptable predictions, its accuracy was lower than that of SVR. The use of partial dependence plots (PDPs) enabled a quantitative assessment of the relative influence of the input parameters, revealing that burnishing penetration depth was the dominant factor for all outputs, accounting for approximately 41–47% of the predicted variations in surface quality. Nevertheless, ball diameter and feed rate also exhibited substantial contributions, each ranging from approximately 23–30%, highlighting their non-negligible roles in DBB process control and optimization. Overall, this study presents a novel machine-learning-based framework for analyzing and optimizing the DBB process to enhance surface quality.
Keywords
Deep Ball Burnishing
Surface Quality
Microhardness
Machine Learning

Subjects

[1] A. Rasti, M. H. Sadeghi, and S. S. Farshi, "An investigation into the effect of surface integrity on the fatigue failure of AISI 4340 steel in different drilling strategies," Engineering Failure Analysis, vol. 95, pp. 66-81, 2019. doi: 10.1016/j.engfailanal.2018.08.022
[2] A. C. Petare and N. K. Jain, "A critical review of past research and advances in abrasive flow finishing process," The International Journal of Advanced Manufacturing Technology, vol. 97, no. 1, pp. 741-782, 2018. doi: 10.1007/s00170-018-1928-7
[3] P. Sender and I. Buj-Corral, "Influence of honing parameters on the quality of the machined parts and innovations in honing processes," Metals, vol. 13, no. 1, p. 140, 2023. doi: 10.3390/met13010140
[4] X. Wang, X. Li, W. Li, and S. Yang, "Role of dry media in influencing performance during processing by centrifugal superfinishing," Materials and Manufacturing Processes, vol. 40, no. 3, pp. 353-364, 2025. doi: 10.1080/10426914.2024.2425618
[5] Y. Yang, W. Li, S. Yang, and X. Li, "Investigation and optimization of parameters for bidirectional composite vibratory finishing of gears," Materials and Manufacturing Processes, vol. 40, no. 4, pp. 547-556, 2025. doi: 10.1080/10426914.2024.2437762
[6] A. M. Mebarek, M. Bourebia, L. Laouar, and N. Bouchelaghem, "Effect of ball burnishing process on surface roughness and corrosion behavior of S235JR steel," The International Journal of Advanced Manufacturing Technology, vol. 130, no. 7, pp. 3431-3444, 2024. doi: 10.1007/s00170-023-12906-9
[7] M. Ozer, K. Dalli, and A. Ozer, "Effect of ball-burnishing on surface integrity and fatigue behaviour of 7175-T6 AA," Materials Science and Technology, vol. 39, no. 2, pp. 248-257, 2023. doi: 10.1080/02670836.2022.2142744
[8] A. Khodabandeh, D. Sayadi, S. Rajabi, M. Khosrojerdi, M. Khajehzadeh, and M. R. Razfar, "Surface integrity and fatigue behavior of AISI 4340 steel after hybrid laser-ultrasonic assisted ball burnishing process," Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science, vol. 238, no. 15, pp. 7607-7626, 2024. doi: 10.1177/09544062241232236
[9] A. Torres, N. Cuadrado, J. Llumà, M. Vilaseca, and J. A. Travieso-Rodriguez, "Influence of the stainless-steel microstructure on tribological behavior and surface integrity after ball burnishing," Materials, vol. 15, no. 24, p. 8829, 2022. doi: 10.3390/ma15248829
[10] H. Pathade et al., "A review on surface integrity of ball burnishing process," Int. J. Res. Publ. Rev, vol. 3, pp. 137-151, 2022. doi: 10.55248/gengpi.2022.3.10.3
[11] R. O. Vaishya et al., "Mathematical modeling and experimental validation of surface roughness in ball burnishing process," Coatings, vol. 12, no. 10, p. 1506, 2022. doi: 10.3390/coatings12101506
[12] M. S. Uddin, C. Hall, R. Hooper, E. Charrault, P. Murphy, and V. Santos, "Finite element analysis of surface integrity in deep ball-burnishing of a biodegradable AZ31B Mg alloy," Metals, vol. 8, no. 2, p. 136, 2018. doi: 10.3390/met8020136
[13] G. Jagadeesh and S. Gangi Setti, "A review on latest trends in ball and roller burnishing processes for enhancing surface finish," Advances in Materials and Processing Technologies, vol. 8, no. 4, pp. 4499-4523, 2022. doi: 10.1080/2374068X.2022.2077534
[14] A. Rasti, J. Kazemi, S. Mohammadi, and A. Zeinolabedin-Beygi, "Deep ball burnishing of Al 2024-T4: insights into surface roughness, microhardness, topography, and chemical alteration," Materials and Manufacturing Processes, vol. 40, no. 16, pp. 2203-2217, 2025. doi: 10.1080/10426914.2025.2575175
[15] S. Dzionk, B. Ścibiorski, and W. Przybylski, "Problems of flaking in strengthening shaft burnishing," in International Scientific-Technical Conference MANUFACTURING, 2019: Springer, pp. 108-121. doi: 10.1007/978-3-030-16943-5_10
[16] J. A. Travieso-Rodríguez, G. Dessein, and H. A. González-Rojas, "Improving the surface finish of concave and convex surfaces using a ball burnishing process," Materials and Manufacturing Processes, vol. 26, no. 12, pp. 1494-1502, 2011. doi: 10.1080/10426914.2010.544819
[17] G. Capilla-González, I. Martínez-Ramírez, D. Díaz-Infante, E. Hernández-Rodríguez, V. Alcantar-Camarena, and A. Saldaña-Robles, "Effect of the ball burnishing on the surface quality and mechanical properties of a TRIP steel sheet," The International Journal of Advanced Manufacturing Technology, vol. 116, no. 11, pp. 3953-3964, 2021. doi: 10.1007/s00170-021-07715-x
[18] C. Priyadarsini, V. V. Ramana, K. A. Prabha, and S. Swetha, "A review on ball, roller, low plasticity burnishing process," Materials Today: Proceedings, vol. 18, pp. 5087-5099, 2019. doi: 10.1016/j.matpr.2019.07.505
[19] Q. Zhang et al., "A review of low‐plasticity burnishing and its applications," Advanced Engineering Materials, vol. 24, no. 11, p. 2200365, 2022. doi: 10.1002/adem.202200365
[20] M. Safari, A. H. Rabiee, and J. Joudaki, "Developing a support vector regression (SVR) model for prediction of main and lateral bending angles in laser tube bending process," Materials, vol. 16, no. 8, p. 3251, 2023. doi: 10.1002/adem.202200365
[21] A. Rasti, A. H. Rabiee, A. Zeinolabedin-Beygi, and M. Yazdani, "Surface Roughness and Microhardness Prediction: A Machine Learning Approach for Electrochemical Grinding," Modares Mechanical Engineering, vol. 25, no. 9, 2025. doi: 10.48311/mme.2025.27590
[22] U. U. N. MRE and Z. NAPOVED, "Use of artificial neural networks in ball burnishing process for the prediction of surface roughness of AA 7075 aluminum alloy," Materiali in tehnologije, vol. 42, no. 5, pp. 215-219, 2008. UDK: 669.715:620.17.112
[23] R. Kluz, K. Antosz, T. Trzepieciński, and M. Bucior, "Modelling the influence of slide burnishing parameters on the surface roughness of shafts made of 42CrMo4 heat-treatable steel," Materials, vol. 14, no. 5, p. 1175, 2021. doi: 10.3390/ma14051175
[24] Z. Kanovic et al., "The modelling of surface roughness after the ball burnishing process with a high-stiffness tool by using regression analysis, artificial neural networks, and support vector regression," Metals, vol. 12, no. 2, p. 320, 2022. doi: 10.3390/met12020320
[25] D. Mahajan and R. Tajane, "The optimization of surface roughnessof Al 6061 using Taguchi method in ball burnishing process," International Journal of Scientific and Research Publications, vol. 3, no. 10, 2013. ISSN: 2250-3153
[26] K. Wakamatsu, K. Takahashi, Y. Koyama, and M. Taniguchi, "Fatigue strength improvement of aluminum alloy with surface defect by ball burnishing," Fatigue & Fracture of Engineering Materials & Structures, vol. 47, no. 9, pp. 3464-3473, 2024. doi: 10.1111/ffe.14372
[27] Z. Liu, Q. Dai, J. Deng, Y. Zhang, and V. Ji, "Analytical modeling and experimental verification of surface roughness in the ultrasonic-assisted ball burnishing of shaft targets," The International Journal of Advanced Manufacturing Technology, vol. 107, no. 7, pp. 3593-3613, 2020. doi: 10.1007/s00170-020-05261-6
[28] D. C. Montgomery, E. A. Peck, and G. G. Vining, Introduction to linear regression analysis. John Wiley & Sons, 2021. ISBN: 978-1-119-57872-7
[29] L. Breiman, J. Friedman, R. A. Olshen, and C. J. Stone, Classification and regression trees. Chapman and Hall/CRC, 2017. doi: 10.1201/9781315139470
[30] L. Breiman, "Random forests," Machine learning, vol. 45, no. 1, pp. 5-32, 2001. doi: 10.1023/A:1010933404324
[31] T. Chen, "XGBoost: A Scalable Tree Boosting System," Cornell University, 2016. doi: 10.1145/2939672.2939785
[32] C. Molnar, Interpretable machine learning. Lulu. com, 2020. ISBN: 978-0-244-76852-2
 
 
Modares Mechanical Engineering
Volume 26, Issue 5
April 2026
Pages 307-323