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Optimization of Multilayer Perceptron Neural Network Structure for Simulating the Effect of Input Variables on the Spring-Back Phenomenon in the Ultrasonic Vibration Assisted Single Point Incremental Forming | ||
| AUT Journal of Mechanical Engineering | ||
| دوره 8، شماره 4، اسفند 2024، صفحه 337-350 اصل مقاله (1.4 M) | ||
| نوع مقاله: Research Article | ||
| شناسه دیجیتال (DOI): 10.22060/ajme.2024.23314.6122 | ||
| نویسندگان | ||
| Mahdi Vahdati* ؛ Seyyed Mojtaba Varedi-Koulaei | ||
| Faculty of Mechanical Engineering, Shahrood University of Technology, Shahrood, Iran | ||
| چکیده | ||
| Applying the ultrasonic vibration to the forming tool in single point incremental forming reduces the forming force, increases the sheet formability, and reduces the spring-back. In the present research, with the aim of minimizing the sheet spring-back, the optimal structure of the multilayer perceptron neural network was extracted using three algorithms which are genetic algorithm, imperialistic competition algorithm and equilibrium optimizer. Analyzing the optimal network with an R-value of 0.99973 and a root mean squared error of 0.0084 shows that the optimized network performs excellently in simulating the considered system. Then, the best network was used to optimize the variables affecting the objective functions. These objective functions include the average of measured depth (Have) and the spring-back coefficient (K). The input variables are: vertical step size, sheet thickness, tool diameter, wall inclination angle, and tool feed rate. The results showed that the optimized multilayer perceptron network can simulate the process with very good precision. Also, the extraction of optimal values shows that the maximum of Have and the minimum of K can be achieved with very good accuracy. Finally, the comparison of three algorithms showed that the performance of the equilibrium optimizer was better in optimizing the neural network structure. On the other hand, in the optimizing process of the input variables, the imperialistic competition algorithm has been more efficient. | ||
| کلیدواژه‌ها | ||
| Incremental Forming؛ Ultrasonic Vibration؛ Spring-Back Phenomenon؛ Artificial Intelligence؛ Evolutionary Algorithms | ||
| مراجع | ||
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[1] Z. Chang, M. Yang, J. Chen, Geometric deviation during incremental sheet forming process: Analytical modeling and experiment, International Journal of Machine Tools and Manufacture, 198 (2024) 104160.
[2] R.S. Bhasker, Y. Kumar, Process capabilities and future scope of Incremental Sheet Forming (ISF), Materials Today: Proceedings, 72(3) (2023) 1014-1019.
[3] A. Qadeer, G. Hussain, M. Alkahtani, J. Buhl, Springback behavior of a metal/polymer laminate in incremental sheet forming: stress/strain relaxation perspective, Journal of Materials Research and Technology, 23 (2023) 1725-1737.
[4] S.U. Deokar, P.K. Jain, P. Tandon, A. Pathak, Analysis of Spring back and Force behavior in Single Point Incremental Sheet Forming though FEA, Materials Today: Proceedings, 18 (2019) 3330-3339.
[5] S. Ashokkumar, S.P.S.S. Sivam, S. Balasubramanian, R.V. Nanditta, Effects of process variables optimization on the quality of parts processed in high speed single point incremental sheet metal forming by ranking algorithm, Materials Today: Proceedings, 45(2) (2021) 1707-1712.
[6] D. Patel, A. Gandhi, A review article on process parameters affecting Incremental Sheet Forming (ISF), Materials Today: Proceedings, 63 (2022) 368-375.
[7] F. Blaha, B. Langenecker, Tensile deformation of zinc crystal under ultrasonic vibration, Naturwiss, 42(20) (1955) 556-556.
[8] T. Jimma, Y. Kasuga, N. Iwaki, O. Miyazawa, E. Mori, K. Ito, H. Hatano, An application of ultrasonic vibration to the deep drawing process, Journal of Materials Processing Technology, 80-81 (1998) 406-412.
[9] J. Tsujino, T. Ueoka, K. Takiguchi, H.S.H. Satoh, K.T.K. Takahashi, Characteristics of bending parts of metal plates using ultrasonic bending systems with a vibration punch and a vibration die, Japanese Journal of Applied Physics, 32 (1993) 2447-2451.
[10] K. Siegert, A. Mock, Wire drawing ultrasonically oscillating dies, Journal of Materials Processing Technology, 60 (1996) 657-660.
[11] M. Susan, L.Gh. Bujoreanu, D.G. Galusca, C. Munteanu, M. Mantu, On the drawing in ultrasonic field of metallic wires with high mechanical resistance, Journal of Optoelectronics and Advanced Materials, 7(2) (2005) 637-645.
[12] M. Vahdati, R. Mahdavinejad, S. Amini, Investigation of the ultrasonic vibration effect in incremental sheet metal forming process, Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture, 231(6) (2017) 971-982.
[13] Y. Long, Y. Li, J. Sun, I. Ille, J. Li, J. Twiefel, Effects of process parameters on force reduction and temperature variation during ultrasonic assisted incremental sheet forming process, International Journal of Advanced Manufacturing Technology, 97 (2018) 13-24.
[14] M. Vahdati, Finite Element Analysis and Experimental Study of the Ultrasonic Vibration-assisted Single Point Incremental Forming (UVaSPIF) Process, Iranian Journal of Materials Forming, 6(2) (2019) 30-41.
[15] Z. Cheng, Y. Li, J. Li, F. Li, P.A. Meehan, Ultrasonic assisted incremental sheet forming: Constitutive modeling and deformation analysis, Journal of Materials Processing Technology, 299 (2022) 117365.
[16] M. Vahdati, R.A. Mahdavinejad, S. Amini, Statistical analysis and optimization of factors affecting the spring-back phenomenon in UVaSPIF process using response surface methodology, International Journal of Advanced Design and Manufacturing Technology, 8(1) (2015) 13-23.
[17] K. Mori, M. Yamamoto, K. Osakada, Determination of hammering sequence in incremental sheet metal forming using a genetic algorithm, Journal of Materials Processing Technology, 60(1-4) (1996) 463-468.
[18] T. Sathish, GAC-ANN Technique for Prediction of Spring Back Effect in Wipe Bending Process of Sheet Metal, Materials Today: Proceedings, 5 (2018) 14448-14457.
[19] S. Moradi, M. Gerdooei, S.M. Varedi-Koulaei, H.G. Nosrati, MLP neural network with an optimal architecture for modeling the ECAP-C process, Neural Computing and Applications, 35 (2022) 2701-2715.
[20] M. Nazari, S.M. Varedi-Koulaei, M. Nazari, Flow characteristics prediction in a flow-focusing microchannel for a desired droplet size using an inverse model: experimental and numerical study, Microfluidics and Nanofluidics, 26(4) (2022) 1-19.
[21] M. Ghazvini, S.M. Varedi-Koulaei, M.H. Ahmadi, M. Kim, Optimization of MLP neural network for modeling flow boiling performance of Al2O3/water nanofluids in a horizontal tube, Engineering Analysis with Boundary Elements, 145 (2022) 363-395.
[22] F. Jarahi, M. Vahdati, R. Abedini, Modeling and Optimization of Shear Strength of the Welded Joint Produced by Ultrasonic Welding of Copper Wire to Aluminum Sheet, Transactions of the Indian Institute of Metals, 77 (2024) 2499-2508.
[23] Minitab software, www.minitab.com
[24] M. Vahdati, M. Sedighi, H. Khoshkish, An analytical model to reduce spring back in Incremental Sheet Metal Forming (ISMF) process, Advanced Materials Research, 83-86 (2010) 1113-1120.
[25] M.H. Nadimi-Shahraki, H. Zamani, Z. Asghari Varzaneh, S. Mirjalili, A Systematic Review of the Whale Optimization Algorithm: Theoretical Foundation, Improvements, and Hybridizations, Archives of Computational Methods in Engineering, 30 (2023) 4113–4159.
[26] J.H. Holland, Adaptation in natural and artificial systems: an introductory analysis with applications to biology, control, and artificial intelligence, MIT press, ISBN: 9780262581110, 1992.
[27] E. Atashpaz-Gargari, C. Lucas, Imperialist competitive algorithm: an algorithm for optimization inspired by imperialistic competition, In IEEE congress on evolutionary computation, (2007) 4661-4667.
[28] A. Faramarzi, M. Heidarinejad, B. Stephens, S. Mirjalili, Equilibrium optimizer: A novel optimization algorithm, Knowledge-Based Systems, 191 (2020) 105190.
[29] D.C.E. Saputra, K. Sunat, T. Ratnaningsih, A New Artificial Intelligence Approach Using Extreme Learning Machine as the Potentially Effective Model to Predict and Analyze the Diagnosis of Anemia, Healthcare, 11(5) (2023) 697. | ||
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