MÔ HÌNH DỰ BÁO VÀ XÁC ĐỊNH CHẾ ĐỘ CẮT TỐI ƯU TRÊN MÁY PHAY CNC DỰA TRÊN PHƯƠNG PHÁP TÍCH HỢP ANN-GA
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Ngày nhận bài: 23/05/21                Ngày hoàn thiện: 22/06/21                Ngày đăng: 22/06/21Tóm tắt
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[1] N. N. Kien, "Applying artificial intelligence methods and Taguchi analysis to determine the cutting parameters when machining on CNC milling machines," Ph.D, Hanoi University of Science & Technology (HUST), Hanoi, 2014.
[2] A. T. Abbas, D. Y. Pimenov, I. N. Erdakov, T. Mikolajczyk, M. S. Soliman, and M. M. El Rayes, "Optimization of cutting conditions using artificial neural networks and the Edgeworth-Pareto method for CNC face-milling operations on high-strength grade-H steel," The International Journal of Advanced Manufacturing Technology, vol. 105, no. 5, pp. 2151-2165, 2019.
[3] B. Lela, D. Bajić, and S. Jozić, "Regression analysis, support vector machines, and Bayesian neural network approaches to modeling surface roughness in face milling," The International Journal of Advanced Manufacturing Technology, vol. 42, no. 11-12, pp. 1082-1088, 2009.
[4] P. Kovac, D. Rodic, V. Pucovsky, B. Savkovic, and M. Gostimirovic, "Application of fuzzy logic and regression analysis for modeling surface roughness in face milliing," Journal of Intelligent manufacturing, vol. 24, no. 4, pp. 755-762, 2013.
[5] D. Bajić, B. Lela, and D. Živković, "Modeling of machined surface roughness and optimization of cutting parameters in face milling," Metalurgija, vol. 47, no. 4, pp. 331-334, 2008.
[6] X. Chen, C. Li, Y. Tang, L. Li, Y. Du, and L. Li, "Integrated optimization of cutting tool and cutting parameters in face milling for minimizing energy footprint and production time," Energy, vol. 175, pp. 1021-1037, 2019.
[7] D. A. Tuan and N. H. That, "Optimizingon surface roughness and materials rate for milling SKD61 hardening steel with taguchi methods and response surface methodology," Journal of Science and Technology - Hung Yen University of Technology and Education, vol. 15, pp. 22-26, 2017.
[8] T. L. Nguyen, T. D. Hoang, and L. Hoang, Multi-objective optimization for high speed milling using PSO algorithm," Proceeding of the 5th National Conference on Mechanical Science & Technology (VCME) Hanoi, 2018, pp. 566-577.
[9] Y.-C. Lin, K.-D. Wu, W.-C. Shih, P.-K. Hsu, and J.-P. Hung, "Prediction of Surface Roughness Based on Cutting Parameters and Machining Vibration in End Milling Using Regression Method and Artificial Neural Network," Applied Sciences, vol. 10, no. 11, pp. 3941, 2020.
[10] D. L. Nguyen, "Prediction Surface Roughness Using Artificial Neural Network Application For Turning Steel C45," Science and Technology journal (Hanoi University of Industry), vol. 43, pp. 62-66, 2017.
[11] B. R. Krishnan, C. M. Sundaram, and A. Vembathurajesh, "Review of Surface Roughness Prediction in Cylindrical Grinding process by using RSM and ANN," International Journal of Recent Trends in Engineering and Research, vol. 4, no. 12, pp. 2455-1457, 2018.
[12] K. S. Sangwan, S. Saxena, and G. Kant, "Optimization of machining parameters to minimize surface roughness using integrated ANN-GA approach," Procedia Cirp, vol. 29, pp. 305-310, 2015.
[13] N. N. Kien, T. V. Dich, V. T. Thang, and N. T. Hieu, "The Artificial Neural Network Method and Artificial Evolution Method for Determining the Optimal Technology Parameter to Manufacture on CNC Milling," Vietnam Journal of Science and Technology, vol. 51, no. 2, p. 259, 2013.
[14] T. T. N. Nguyen, "Research on algorithms to find optimal solutions in the process of training neural networks to identify and control nonlinear dynamic objects," Ph.D, University of Economics - Technology for Industries, 2011.
[15] G. Zhang, B. E. Patuwo, and M. Y. Hu, "Forecasting with artificial neural networks: The state of the art," International journal of forecasting, vol. 14, no. 1, pp. 35-62, 1998.
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DOI: https://doi.org/10.34238/tnu-jst.4533
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