Combining the Entropy Method and Genetic Algorithm in the Multi-Objective Grinding Process
Received: 21 January 2025 | Revised: 09 February 2025 | Accepted: 14 February 2025 | Online: 4 March 2025
Corresponding author: Nguyen Trong Mai
Abstract
Grinding is a commonly used method for machining products that require high precision in the mechanical engineering industry. This study conducts multi-objective optimization of the grinding process for SUS440C steel on a surface grinding machine. A total of 15 experiments were designed by deploying the Box-Behnken method. In each experiment, the values of three cutting parameters, namely workpiece speed, feed rate, and depth of cutting, varied, while four objectives, involving surface roughness (Ra), cutting force component in the x-direction (Fx), cutting force component in the y-direction (Fy), and cutting force component in the z-direction (Fz), were measured. The entropy method was used to calculate the weights of the objectives, and the Genetic Algorithm (GA) was employed to solve the multi-objective optimization problem. According to the results, the optimal values of 5 m/min, 3 mm/stroke, and 0.0198 mm were, respectively, obtained for the workpiece speed, feed rate, and depth of cut. Corresponding to these cutting parameter optimal values, the values attained for the Ra, Fx, Fy, and Fz objectives were 0.612 mm, 10.126 N, 13.621 N, and 4.112 N, respectively.
Keywords:
surface grinding, SUS440C steel, multi-objective optimization, entropy method, genetic algorithmDownloads
References
X. Li, Z. Tang, C. Chen, L. Wang, Y. Zhang, and D. Wang, "An optimization method of grinding wheel profile for complex large shaft curve grinding," Journal of Advanced Manufacturing Science and Technology, vol. 5, no. 2, Aug. 2024, Art. no. 2025008.
D. Kumar Patel, D. Goyal, and B. S. Pabla, "Optimization of parameters in cylindrical and surface grinding for improved surface finish," Royal Society Open Science, vol. 5, no. 5, May 2018, Art. no. 171906, https://doi.org/10.1098/rsos.171906.
D. D. Trung and N. T. Mai, "Improving the Accuracy of the Surface Roughness Model in Grinding Through Square Root Transformation," International Journal of Mechanical Engineering and Robotics Research, vol. 13, no. 2, pp. 249–253, 2024.
P. Biswas et al., "An Experimental Analysis of Grinding Parameters and Conditions on Surface Roughness of Finished Product," Journal of Physics: Conference Series, vol. 2286, no. 1, Apr. 2022, Art. no. 012027.
A. Heininen, S. Santa-aho, J. Röttger, P. Julkunen, and K. T. Koskinen, "Grinding optimization using nondestructive testing (NDT) and empirical models," Machining Science and Technology, vol. 28, no. 1, pp. 98–118, Jan. 2024.
Vu Ngoc Pi, Luu Anh Tung, Le Xuan Hung, and Banh Tien Long, "Cost Optimization of Surface Grinding Process," Journal of Environmental Science and Engineering A, vol. 5, no. 12, pp. 606–611, Dec. 2016.
S. Henkel et al., "Optimization of grinding processes on fused silica components using in-process vibrometry and dynamometer measurements," EPJ Web of Conferences, vol. 266, 2022, Art. no. 03011.
M. D. Abyaneh, P. Narimani, M. Hadad, and S. Attarsharghi, "Using machine learning and optimization for controlling surface roughness in grinding of St37," ENERGY QUIPSYS, vol. 11, no. 3, pp. 321–337, Sep. 2023.
M. M. Salem, S. S. Mohamed, and A. A. Ibrahim, "Optimization of Surface Grinding Parameters Used in Improved Surface Integrity," International Research Journal of Innovations in Engineering and Technology, vol. 6, no. 5, pp. 124–130, May 2022.
B. Dasthagiri and E. V. Goud, “Optimization Studies on Surface Grinding Process Parameters," International Journal of Innovative Research in Science, Engineering and Technology, vol. 4, no. 7, pp. 6148–6156, 2007.
T. H. Danh and L. H. Ky, "Optimization of Wheel Dressing Technological Parameters when Grinding Hardox 500 Steel," Engineering, Technology & Applied Science Research, vol. 14, no. 4, pp. 15854–15859, Aug. 2024.
D. D. Trung, N. V. Thien, and N.-T. Nguyen, "Application of TOPSIS Method in Multi-Objective Optimization of the Grinding Process Using Segmented Grinding Wheel," Tribology in Industry, vol. 43, no. 1, pp. 12–22, Mar. 2021.
D. T. Do, X. T. Hoang, and D. H. Le, "Improving the Efficiency of Grinding Process Using the Rubber-Pasted Grinding Wheel," Strojnícky časopis - Journal of Mechanical Engineering, vol. 72, no. 1, pp. 23–34, May 2022.
D. D. Trung, N.-T. Nguyen, D. H. Tien, and H. L. Dang, "A Research on Multi-Objective Optimization of the Grinding Process Using Segmented Grinding Wheel by Taguchi-Dear Method," EUREKA: Physics and Engineering, no. 1, pp. 67–77, Jan. 2021.
Z. Chen et al., "The optimization of accuracy and efficiency for multistage precision grinding process with an improved particle swarm optimization algorithm," International Journal of Advanced Robotic Systems, vol. 17, no. 1, Jan. 2020, Art. no. 1729881419893508.
R. Roy et al., "Multi-Response Optimization of Surface Grinding Process Parameters of AISI 4140 Alloy Steel Using Response Surface Methodology and Desirability Function under Dry and Wet Conditions," Coatings, vol. 12, no. 1, Jan. 2022, Art. no. 104.
R. Rekha, N. Baskar, M. R. A. Padmanaban, and A. Palanisamy, "Optimization of cylindrical grinding process parameters using meta-heuristic algorithms," Indian Journal of Engineering and Materials Sciences (IJEMS), vol. 27, no. 2, pp. 389–395, Mar. 2021.
N.-T. Nguyen and D. D. Trung, "Combination of Taguchi method, MOORA and COPRAS techniques in multi-objective optimization of surface grinding process," Journal of Applied Engineering Science, vol. 19, no. 2, pp. 390–398, 2021.
A. Gürgen et al., "Optimization of CNC operating parameters to minimize surface roughness of Pinus sylvestris using integrated artificial neural network and genetic algorithm," Maderas. Ciencia y tecnología, vol. 24, 2022.
V. Nguyen, D. Nguyen, D. Do, and T. Tran, "Multi-Objective Optimization of the Surface Grinding Process for Heat-Treated Steel," International Journal of Automotive and Mechanical Engineering, vol. 21, no. 4, pp. 11831–11843, Dec. 2024.
G. Xiao, S. Yang, W. Wang, and Z. Yang, "Parameter Optimization and Experimental Study of Orderly Arrangement Grinding Wheel," International Journal of Mechatronics and Applied Mechanics, vol. I, no. 17, pp. 77–86, Sep. 2024.
N.-T. Nguyen, D. D. Trung, N.-T. Nguyen, and D. D. Trung, "A study on the surface grinding process of the SUJ2 steel using CBN slotted grinding wheel," AIMS Materials Science, vol. 7, no. 6, pp. 871–886, 2020.
D. D. Trung, "Influence of Cutting Parameters on Surface Roughness in Grinding of 65G Steel," Tribology in Industry, vol. 43, no. 1, pp. 167–176, Mar. 2021.
D. D. Trung, "A combination method for multi-criteria decision making problem in turning process," Manufacturing Review, vol. 8, p. 26, Dec. 2021.
D. D. Trung and H. X. Thinh, "A multi-criteria decision-making in turning process using the MAIRCA, EAMR, MARCOS and TOPSIS methods: A comparative study," Advances in Production Engineering & Management, vol. 16, no. 4, pp. 443–456, Dec. 2021.
E. K. Zavadskas and V. Podvezko, "Integrated Determination of Objective Criteria Weights in MCDM," International Journal of Information Technology & Decision Making, vol. 15, no. 02, pp. 267–283, Mar. 2016.
S. Katoch, S. S. Chauhan, and V. Kumar, "A review on genetic algorithm: past, present, and future," Multimedia Tools and Applications, vol. 80, no. 5, pp. 8091–8126, Feb. 2021
S. R. ABBAS, "Genetic Algorithm and Particle Swarm Optimization Techniques in Multimodal Functions Optimization," MS Thesis, Near East University, Nicosia, 2017.
H. K. Nguyen, P. V. Dong, and V. Q. Tran, "Investigation of influence of grinding wheel and cutting parameters on surface roughness and surface hardening when relieving grinding the gear milling teeth surface based on the Archimedes’ spiral," International Journal of Metrology and Quality Engineering, vol. 14, Art. no. 1, 2023.
M. Aravind and S. Periyasamy, "Optimization of Surface Grinding Process Parameters By Taguchi Method And Response Surface Methodology," International Journal of Engineering Research, vol. 3, no. 5, pp. 1721–1727, 2014.
S. Malkin and C. Guo, Grinding Technology: Theory and Application of Machining with Abrasives. Industrial Press Inc., 2008.
I. D. Marinescu, M. P. Hitchiner, E. Uhlmann, W. B. Rowe, and I. Inasaki, Handbook of Machining with Grinding Wheels. CRC Press, 2016.
T. Yin, H. Zhang, W. Hang, and S. To, "A Novel Approach to Optimizing Grinding Parameters in the Parallel Grinding Process," Processes, vol. 12, no. 3, Mar. 2024, Art. no. 493.
W. Kacalak, D. Lipiński, and F. Szafraniec, "Selected Aspects of Precision Grinding Processes Optimization," Materials, vol. 17, no. 3, Jan. 2024, Art. no. 607.
N. T. Mai, T. D. Quy, and N. V. Manh, "Optimizing Cutting Parameters for Surface Grinding of SUS440C Steel," International Research Journal of Advanced Engineering and Science, vol. 9, no. 4, pp. 351–354, 2024.
P. B. Khoi, D. D. Trung, N. Cuong, and N. D. Man, "Research on Optimization of Plunge Centerless Grinding Process using Genetic Algorithm and Response Surface Method," International Journal of Scientific Engineering and Technology, vol. 4, no. 3, pp. 207–211, Mar. 2015.
N. T. Binh, Optimizing the Metal Cutting Process. Education Publishing House, 2013.
Downloads
How to Cite
License
Copyright (c) 2025 Nguyen Trong Mai
This work is licensed under a Creative Commons Attribution 4.0 International License.
Authors who publish with this journal agree to the following terms:
- Authors retain the copyright and grant the journal the right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) after its publication in ETASR with an acknowledgement of its initial publication in this journal.
