Modares Mechanical Engineering

Modares Mechanical Engineering

Surface Roughness and Microhardness Prediction: A Machine Learning Approach for Electrochemical Grinding

Document Type : Original Research

Authors
Amir Rasti 1
Amir Hossein Rabiee 2
Ali Zeinolabedin-Beygi 1
Mohammad Yazdani 1
1 Advanced Technology of Machine Tools Laboratory (ATMT), Faculty of Mechanical Engineering, Tarbiat Modares University, Tehran, Iran
2 Department of Mechanical Engineering, Arak University of Technology, Arak, Iran
10.48311/mme.2025.27590
Abstract
This study focuses on the development of predictive machine learning models to estimate key surface integrity parameters in the electrochemical grinding (ECG) of AISI 304 stainless steel. Experimental data were collected from a series of 20 controlled tests based on a response surface methodology (RSM) design, varying three primary process parameters: voltage, electrolyte concentration, and grinding wheel speed. Using this dataset, Gaussian Process Regression (GPR) models were constructed for four output variables: current density, surface roughness in X- and Y-directions (Ra_x and Ra_y), and surface microhardness (Vickers). Model performance was evaluated using R² scores, residual analysis, and error distributions across both training and test datasets. The results demonstrate that surface roughness parameters, particularly Ra_y (R²_test = 0.970) and Ra_x (R²_test = 0.932), were predicted with the highest accuracy and consistency. Current density also exhibited strong performance (R²_test = 0.954), though with minor deviations at extreme values. Surface microhardness, in contrast, posed greater modeling challenges, achieving the lowest test R² (0.843) and showing systematic underprediction. Residual and error analyses confirmed these trends, with minimal bias and variance for Ra_x and Ra_y, and broader, asymmetric error profiles for hardness. The least roughness was observed under an electrolyte concentration of 140 g/L, an applied voltage of 20 V, and a grinding wheel rotational speed of 2000 rpm. Overall, the GPR models proved effective for capturing ECG process behavior and offer potential for process optimization in precision manufacturing.
Keywords
Electrochemical Grinding
Surface Integrity
Machine Learning
Gaussian Process Regression
Subjects
[1]   M. K. Firouzjaei, H. M. Naeini, M. M. Kasaei, M. J. Mirnia, and L. F. da Silva, "A microscale constitutive model for thin stainless steel sheets considering size effect," Proceedings of the Institution of Mechanical Engineers, Part L: Journal of Materials: Design and Applications, vol. 237, no. 10, pp. 2104-2114, 2023. doi:10.1177/14644207231169456
[2]   M. Karimi Firouzjaei, H. Moslemi Naeini, M. M. Kasaei, and M. J. Mirnia, "A constitutive model for stainless steel 304 sheet considering size effect in micro-scale," Modares Mechanical Engineering, vol. 22, no. 8, pp. 519-528, 2022. doi:10.52547/mme.22.8.519
[3]   M. K. Firouzjaei, H. M. Naeini, M. M. Kasaei, M. J. Mirnia, and L. F. da Silva, "Microscale modeling of the ductile fracture behavior of thin stainless steel sheets," Thin-Walled Structures, vol. 196, p. 111457, 2024. doi:10.1016/j.tws.2023.111457
[4]   R. Narayan, "Medical application of stainless steels," ASM handbook, vol. 23, pp. 199-210, 2012. doi:10.31399/asm.hb.v23.a0005673
[5]   D. E. Wert and R. P. DiSabella, "Strong, corrosion-resistant stainless steel: this strong, tough, corrosion-resistant stainless steel improves performance of equipment in aerospace, medical instruments, oil drilling, firearms, and marine applications," Advanced materials & processes, vol. 164, no. 8, pp. 34-37, 2006.
[6]   R. E. Ilhan, Analysis, optimization, and mechanistic modelling of electrochemical surface grinding (ECG) process. Lehigh University, 1991.
[7]   D. S. Patel, V. K. Jain, and J. Ramkumar, "Electrochemical grinding," in Nanofinishing science and technology: CRC Press, 2016, pp. 341-372.
[8]   P. Thanki, "Electrochemical grinding process, current state and future direction: A systematic literature review," Int. J. Appl. Eng. Res., vol. 9, no. 6, pp. 637-644, 2014.
[9]   B. Bhuyan, C. Garg, and L. Gupta, "Design and development of tabletop electrochemical grinding setup," Materials Today: Proceedings, vol. 21, pp. 1479-1482, 2020. doi:10.1016/j.matpr.2019.11.058
[10] T. Maksoud and A. Brooks, "Electrochemical grinding of ceramic form tooling," Journal of materials processing technology, vol. 55, no. 2, pp. 70-75, 1995. doi:10.1016/0924-0136(95)01787-9
[11] L. Hansong, F. Shuxing, Q. Zhang, N. Shen, and Q. Ningsong, "Simulation and experimental investigation of inner-jet electrochemical grinding of GH4169 alloy," Chinese Journal of Aeronautics, vol. 31, no. 3, pp. 608-616, 2018. doi:10.1016/j.cja.2017.08.014
[12] H. M. Yehia, M. Hakim, and A. El-Assal, "Effect of the Al2O3 powder addition on the metal removal rate and the surface roughness of the electrochemical grinding machining," Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture, vol. 234, no. 12, pp. 1538-1548, 2020. doi:10.1177/0954405420921721
[13] R. Levinger and S. Malkin, "Electrochemical grinding of WC-Co cemented carbides," 1979.
[14] S. Li, Y. Wu, M. Nomura, and T. Fujii, "Fundamental machining characteristics of ultrasonic-assisted electrochemical grinding of Ti–6Al–4V," Journal of Manufacturing Science and Engineering, vol. 140, no. 7, p. 071009, 2018. doi:10.1115/1.4039855
[15] M. Hakima, M. Yehiab, A. El-Assal, and K. Abdelwahed, "Hybrid Abrasive Electrochemical Grinding Machining of alloy steel K110," Engineering Research Journal, vol. 163, pp. 294-308, 2019. doi:10.21608/erj.2019.122540
[16] P. Sapre, A. Mall, and S. S. Joshi, "Analysis of electrolytic flow effects in micro-electrochemical grinding," Journal of manufacturing science and engineering, vol. 135, no. 1, p. 011012, 2013. doi:10.1115/1.4023266
[17] A. Zeinolabedin-Beygi, H. M. Naeini, H. Talebi-Ghadikolaee, A. H. Rabiee, and S. Hajiahmadi, "Predictive modeling of spring-back in pre-punched sheet roll forming using machine learning," The Journal of Strain Analysis for Engineering Design, vol. 59, no. 7, pp. 463-474, 2024. doi:10.1177/03093247241263685
[18] H. Ziaiefar, M. Amiryan, M. Ghodsi, F. Honarvar, and Y. Hojjat, "Ultrasonic damage classification in pipes and plates using wavelet transform and SVM," Modares Mechanical Engineering, vol. 15, no. 5, pp. 41-48, 2015.
[19] 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:10.3390/ma16083251
[20] D. K. Rahi and A. K. Dubey, "Modeling and optimization of surface quality characteristics in electrochemical surface grinding of metal matrix composite," Journal of Materials Engineering and Performance, vol. 33, no. 9, pp. 4345-4358, 2024. doi:10.1007/s11665-023-08267-9
[21] A. Cebi, H. Demirtas, and A. Kaleli, "Implementation of robotic electrochemical machining in freeform surface machining with material removal rate prediction using different machine learning algorithms," Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science, vol. 238, no. 9, pp. 3835-3849, 2024. doi:10.1177/09544062231208302
[22] M. Yazdani and A. Rasti, "Assessment on surface integrity in electrochemical grinding of AISI 304," Heliyon, vol. 11, no. 1, 2025. doi:10.1016/j.heliyon.2024.e41435
[23] C. K. Williams and C. E. Rasmussen, Gaussian processes for machine learning (no. 3). MIT press Cambridge, MA, 2006.
[24] M. L. Stein, Interpolation of spatial data: some theory for kriging. Springer Science & Business Media, 1999.
[25] C. Williams and C. Rasmussen, "Gaussian processes for regression," Advances in neural information processing systems, vol. 8, 1995.
[26] H. Talebi-Ghadikolaee, H. Moslemi Naeini, A. H. Rabiee, A. Zeinolabedin Beygi, and S. Alexandrov, "Experimental-numerical analysis of ductile damage modeling of aluminum alloy using a hybrid approach: ductile fracture criteria and adaptive neural-fuzzy system (ANFIS)," International Journal of Modelling and Simulation, vol. 43, no. 5, pp. 736-751, 2023. doi: 10.1080/02286203.2022.2121675
Modares Mechanical Engineering
Volume 25, Issue 9
September 2025
Pages 587-594