Multi-Objective Optimization of Material Removal Rate and Surface Roughness in Ultrasonic Vibration-Assisted EDM Using NSGA-II, GPR, and AHP
Received: 9 April 2025 | Revised: 25 May 2025 | Accepted: 1 June 2025 | Online: 2 August 2025
Corresponding author: Thi Phuong Thao Tran
Abstract
Ultrasonic Vibration-Assisted Electrical Discharge Machining (UV-EDM) constitutes a promising technique for improving machining efficiency and surface quality, particularly when working with difficult-to-machine materials. This study presents a comprehensive multi-objective optimization approach for UV-EDM applied to 90CrSi steel, aiming to maximize the Material Removal Rate (MRR) while minimizing Surface Roughness (Ra). The experimental data were collected under varying process parameters, including the peak current, pulse-on time, and ultrasonic vibration amplitude. A Gaussian Process Regression (GPR) model was developed to accurately predict MRR and Ra. These predictive models were then integrated into the Non-Dominated Sorting Genetic Algorithm II (NSGA-II) to perform Pareto-based optimization. Additionally, the Analytic Hierarchy Process (AHP) was employed to rank the Pareto-optimal solutions based on decision-makers’ preferences. The results demonstrate the effectiveness of combining GPR and NSGA-II for modeling and optimizing UV-EDM, while the use of AHP enables a rational selection of optimal machining conditions. This hybrid methodology offers valuable insights into enhancing productivity and surface integrity in precision machining applications.
Keywords:
ultrasonic vibration assisted EDM, material removal rate, surface roughness, NSGA-II, Gaussian process regression, AHP, multi objective optimizationDownloads
References
A. Hirao, H. Gotoh, and T. Tani, "Some Effects on EDM Characteristics by Assisted Ultrasonic Vibration of the Tool Electrode,"Procedia CIRP, vol. 68, pp. 76–80, Jan. 2018. DOI: https://doi.org/10.1016/j.procir.2017.12.025
J. Singh, R. Walia, P. Satsangi, and V. Singh, "FEM modeling of ultrasonic vibration assisted work-piece in EDM process," International Journal of Mechanic Systems Engineering, vol. 1, no. 1, pp. 8–16, Jan, 2011.
P. Zhang et al., "Investigating mechanisms of debris removal in ultrasonic vibration-assisted EDM drilling," International Journal of Mechanical Sciences, vol. 279, Oct. 2024, , Art. no. 109486. DOI: https://doi.org/10.1016/j.ijmecsci.2024.109486
Z. Yin et al., "A novel EDM method using longitudinal-torsional ultrasonic vibration (LTV) electrodes to improve machining performance for micro-holes," Journal of Manufacturing Processes, vol. 102, pp. 231–243, Sep. 2023. DOI: https://doi.org/10.1016/j.jmapro.2023.07.023
J. Tang, Z. Li, and J. Bai, "Investigation of depth error of microgroove in micro-EDM adopting ultrasonic circular vibration (UCV) electrode," The International Journal of Advanced Manufacturing Technology, vol. 131, no. 7, pp. 4009–4020, Apr. 2024. DOI: https://doi.org/10.1007/s00170-024-13283-7
Y. Wang, L. Fan, J. Shi, Y. Dong, and Z. Fu, "Effect of cavitation on surface formation mechanism of ultrasonic vibration-assisted EDM," The International Journal of Advanced Manufacturing Technology, vol. 124, no. 10, pp. 3645–3656, Feb. 2023. DOI: https://doi.org/10.1007/s00170-022-10780-5
M. R. Shabgard, A. Gholipoor, and M. Mohammadpourfard, "Numerical and experimental study of the effects of ultrasonic vibrations of tool on machining characteristics of EDM process," The International Journal of Advanced Manufacturing Technology, vol. 96, no. 5, pp. 2657–2669, May 2018. DOI: https://doi.org/10.1007/s00170-017-1487-3
C. Praneetpongrung, Y. Fukuzawa, S. Nagasawa, and K. Yamashita, "Effects of the Edm Combined Ultrasonic Vibration on the Machining Properties of Si3N4," Materials Transactions, vol. 51, no. 11, pp. 2113–2120, Nov. 2010. DOI: https://doi.org/10.2320/matertrans.M2010194
Y.-C. Lin, J.-C. Hung, H.-M. Chow, A.-C. Wang, and J.-T. Chen, "Machining Characteristics of a Hybrid Process of EDM in Gas Combined with Ultrasonic Vibration and AJM," Procedia CIRP, vol. 42, pp. 167–172, Jan. 2016. DOI: https://doi.org/10.1016/j.procir.2016.02.213
T. P. T. Le, V. T. Dinh, T. Q. D. Nguyen, D. B. Vu, and T. T. Vu, "Application of the Multi-Criteria Decision Method to Find the Best Input Factors for Electrical Discharge Machining 90CrSi Tool Steel using Graphite Electrodes," Engineering, Technology & Applied Science Research, vol. 14, no. 6, pp. 18883–18888, Dec. 2024. DOI: https://doi.org/10.48084/etasr.9114
S. K. Ghazi, M. A. Abdullah, and H. H. Abdulridha, "Investigating the Impact of EDM Parameters on Surface Roughness and Electrode Wear Rate in 7024 Aluminum Alloy," Engineering, Technology & Applied Science Research, vol. 15, no. 1, pp. 19401–19407, Feb. 2025. DOI: https://doi.org/10.48084/etasr.9252
A. R. Hayyawi, H. Al-Ethari, and A. H. Haleem, "Optimization of the PM-EDM Process Parameters for Ti-35Nb-7Zr-5Ta Bio Alloy," Engineering, Technology & Applied Science Research, vol. 14, no. 3, pp. 13982–13989, Jun. 2024. DOI: https://doi.org/10.48084/etasr.6845
G. E. P. Box and D. W. and Behnken, "Some New Three Level Designs for the Study of Quantitative Variables," Technometrics, vol. 2, no. 4, pp. 455–475, Nov. 1960. DOI: https://doi.org/10.1080/00401706.1960.10489912
J. Lei, H. Shen, H. Wu, W. Pan, X. Wu, and C. Zhao, "Ultrasonic vibration-assisted electrical discharge machining of enclosed microgrooves with laminated electrodes," Journal of Materials Research and Technology, vol. 30, pp. 9521–9530, May 2024. DOI: https://doi.org/10.1016/j.jmrt.2024.06.035
G. E. P. Box and D. R. Cox, "An Analysis of Transformations," Journal of the Royal Statistical Society Series B: Statistical Methodology, vol. 26, no. 2, pp. 211–243, Jul. 1964. DOI: https://doi.org/10.1111/j.2517-6161.1964.tb00553.x
C. E. Rasmussen and C. K. I. Williams, Gaussian Processes for Machine Learning, 1st ed. Cambridge, MA, USA: MIT Press, 2005. DOI: https://doi.org/10.7551/mitpress/3206.001.0001
K. Deb, A. Pratap, S. Agarwal, and T. Meyarivan, "A fast and elitist multiobjective genetic algorithm: NSGA-II," IEEE Transactions on Evolutionary Computation, vol. 6, no. 2, pp. 182–197, Apr. 2002. DOI: https://doi.org/10.1109/4235.996017
T. L. Saaty, Decision Making with Dependence and Feedback: The Analytic Network Process : the Organization and Prioritization of Complexity, 2nd ed. Maidenhead, Berkshire, UK: RWS Publications, 1996.
M. K. Das, K. Kumar, T. Kr. Barman, and P. Sahoo, "Application of Artificial Bee Colony Algorithm for Optimization of MRR and Surface Roughness in EDM of EN31 Tool Steel," Procedia Materials Science, vol. 6, pp. 741–751, Jan. 2014. DOI: https://doi.org/10.1016/j.mspro.2014.07.090
X. Gao, J. Li, Q. Xing, and Q. Zhang, "Research on ultrasonic vibration–assisted electrical discharge machining SiCp/Al composite," The International Journal of Advanced Manufacturing Technology, vol. 121, no. 3, pp. 2095–2113, Jul. 2022. DOI: https://doi.org/10.1007/s00170-022-09352-4
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Copyright (c) 2025 Van Thanh Dinh, Thu Quy Le, Thi Tam Do, Ngoc Pi Vu, Thi Phuong Thao Tran
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