Multi-Objective Optimization of the Turning Process using the Probability Method
Received: 1 November 2024 | Revised: 28 November 2024 | Accepted: 11 December 2024 | Online: 17 December 2024
Corresponding author: Hoang Xuan Thinh
Abstract
This research aims to determine the optimal values of the cutting parameters when solving the turning process multi-objective problem. Three cutting parameters are considered in this study: spindle speed (nw), feed rate (f), and depth of cut (ap). A turning experiment series was conducted on a conventional lathe, with nine experiments having been designed according to the Taguchi experimental design matrix. In each experiment, the values of the three parameters changed and the material Removal Rate (Q) was measured. The Probability method was used to solve the multi-objective optimization problem. The Method based on the Removal Effects of Criteria (MEREC) technique was employed to calculate the weights of the criteria. The results of the optimization problem using the Probability method were also compared with those obtained using other methods, including the Simple Additive Weighting (SAW), Technique for Order Preference by Similarity to Ideal Solution (TOPSIS), Vlsekriterijumska optimizacijaI KOmpromisno Resenje (VIKOR), Multi-Atributive Ideal-Real Comparative Analysis (MAIRCA), Evaluation by an Area-based Method for Ranking (EAMR), Complex Proportional Assessment (COPRAS), Measurement Alternatives and Ranking according to Compromise Solution (MARCOS), Proximity Indexed Value (PIV), and Combined Compromise Solution (COCOSO). All the methods converged on the same unique solution to the multi-objective optimization problem. The optimal values for the parameters were: nw = 1350 rev/min, corresponding to a feed rate of 0.13 mm/rev, and a depth of cut of 0.4 mm. When machining with these optimal cutting parameters, the resulting values for Ra, RE, and Q were 1.057 µm, 0.03 mm, and 13225.68 mm²/min, respectively.
Keywords:
turning, multi-objective optimization, probability method, MEREC method, MCDMDownloads
References
L. Alessandretti, L. G. Natera Orozco, M. Saberi, M. Szell, and F. Battiston, "Multimodal urban mobility and multilayer transport networks," Environment and Planning B: Urban Analytics and City Science, vol. 50, no. 8, pp. 2038–2070, Oct. 2023.
Y. Zhou, C. Cao, and Z. Feng, "Optimization of Multimodal Discrete Network Design Problems Based on Super Networks," Applied Sciences, vol. 11, no. 21, Jan. 2021, Art. no. 10143.
P. Modesti and A. Sciomachen, "A utility measure for finding multiobjective shortest paths in urban multimodal transportation networks1," European Journal of Operational Research, vol. 111, no. 3, pp. 495–508, Dec. 1998.
T. Y. Ma, "An A* Label-setting Algorithm for Multimodal Resource Constrained Shortest Path Problem," Procedia - Social and Behavioral Sciences, vol. 111, pp. 330–339, Feb. 2014.
Y. Luo, Y. Zhang, J. Huang, and H. Yang, "Multi-route planning of multimodal transportation for oversize and heavyweight cargo based on reconstruction," Computers & Operations Research, vol. 128, Apr. 2021, Art. no. 105172.
M. Bielli, A. Boulmakoul, and H. Mouncif, "Object modeling and path computation for multimodal travel systems," European Journal of Operational Research, vol. 175, no. 3, pp. 1705–1730, Dec. 2006.
M. G. Battista, M. Lucertini, and B. Simeone, "Path Composition and Multiple Choice in a Bimodal Transportation Network. Volume 2: Modelling Transport Systems," presented at the World Transport Research. Proceedings of the 7th World Conference on Transport ResearchWorld Conference on Transport Research Society, 1996.
A. Idri, M. Oukarfi, A. Boulmakoul, K. Zeitouni, and A. Masri, "A new time-dependent shortest path algorithm for multimodal transportation network," Procedia Computer Science, vol. 109, pp. 692–697, Jan. 2017.
O. Dib, L. Moalic, M.-A. Manier, and A. Caminada, "An advanced GA–VNS combination for multicriteria route planning in public transit networks," Expert Systems with Applications, vol. 72, pp. 67–82, Apr. 2017.
O. Dib, M. Dib, and A. Caminada, "Computing Multicriteria Shortest Paths in Stochastic Multimodal Networks Using a Memetic Algorithm," International Journal on Artificial Intelligence Tools, vol. 27, no. 07, Nov. 2018, Art. no. 1860012.
H. Ayed, C. Galvez-Fernandez, Z. Habbas, and D. Khadraoui, "Solving time-dependent multimodal transport problems using a transfer graph model," Computers & Industrial Engineering, vol. 61, no. 2, pp. 391–401, Sep. 2011.
R. Wang, K. Yang, L. Yang, and Z. Gao, "Modeling and optimization of a road–rail intermodal transport system under uncertain information," Engineering Applications of Artificial Intelligence, vol. 72, pp. 423–436, Jun. 2018.
A. Abbassi, A. E. hilali Alaoui, and J. Boukachour, "Robust optimisation of the intermodal freight transport problem: Modeling and solving with an efficient hybrid approach," Journal of Computational Science, vol. 30, pp. 127–142, Jan. 2019.
H. C. Yu and F. Lu, "A multi-modal route planning approach with an improved genetic algorithm," in Advances in Geo-Spatial Information Science, CRC Press, 2012.
H. Jafarzadeh, N. Moradinasab, and M. Elyasi, "An Enhanced Genetic Algorithm for the Generalized Traveling Salesman Problem," Engineering, Technology & Applied Science Research, vol. 7, no. 6, pp. 2260–2265, Dec. 2017.
H. Faroqi, "Multiobjective route finding in a multimode transportation network by NSGA-II," Journal of Engineering and Applied Science, vol. 71, no. 1, Mar. 2024, Art. no. 81.
C. N. E. H. Khelifa and A. Belmadani, "New Approach for Continuous and Discrete Optimization: Optimization by Morphological Filters," in Heuristics for Optimization and Learning, F. Yalaoui, L. Amodeo, and E. G. Talbi, Eds. Springer International Publishing, 2021, pp. 425–440.
S. Zaoui and A. Belmadani, "Solving Engineering Optimization Problems Without Penalty," International Journal of Computational Methods, vol. 18, no. 04, May 2021, Art. no. 2150007.
S. Zaoui and A. Belmadani, "Solution of combined economic and emission dispatch problems of power systems without penalty," Applied Artificial Intelligence, vol. 36, no. 1, Dec. 2022, Art. no. 1976092.
S. A. Arhin, A. Gatiba, M. Anderson, B. Manandhar, and M. Ribbisso, "Acceptable Wait Time Models at Transit Bus Stops," Engineering, Technology & Applied Science Research, vol. 9, no. 4, pp. 4574–4580, Aug. 2019.
"TCL - Transports en commun à Lyon : métro, tramway, funiculaire et bus." https://www.tcl.fr/.
"Plans du réseau | TCL." https://www.tcl.fr/plans-du-reseau.
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Copyright (c) 2024 Tran Van Dua, Hoang Xuan Thinh, Nguyen Chi Bao, Duong Van Duc, Tran Minh Hoang
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