Prediction and optimization of load and torque in ring rolling process through development of artificial neural network and evolutionary algorithms

Document Type: Research Paper


1 Department of Mechanical and Aerospace Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran

2 School of Mechanical Engineering, College of Engineering, University of Tehran, Tehran, Iran


Developing artificial neural network (ANN), a model to make a correct prediction of required force and torque in ring rolling process is developed for the first time. Moreover, an optimal state of process for specific range of input parameters is obtained using Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) methods. Radii of main roll and mandrel, rotational speed of main roll, pressing velocity of mandrel and blank size are considered as input parameters. Furthermore, the required load and torque in ring rolling process are taken into account as process outputs. Various three dimensional finite element simulations are performed for different sets of process variables to achieve preliminary data for training and validation of the neural network. Besides, the finite element model is approved via comparison with the experimental results of the other investigators. The Back Propagation (BP) algorithm is considered to develop Levenberg–Marquardt feed-forward network. Additionally, Model responses analysis is carried out to improve the understanding of the behavior of the ANN model. It is concluded that results of ANN predictions have an appropriate conformity with those from simulation and experiments. Moreover, GA and PSO methods have been implemented to obtain the optimal state of process while their outcomes have been also compared.


Main Subjects

[1]           J. S. Ryoo, D. Y. Yang, W. Johnson, The influence of process parameters on torque and load in ring rolling, Journal of Mechanical Working Technology, Vol. 12, No. 3, pp. 307-321, 1986.

[2]           A. Parvizi, K. Abrinia, A two dimensional Upper Bound Analysis of the ring rolling process with experimental and FEM verifications, International Journal of Mechanical Sciences, Vol. 79, pp. 176-181, 2014.

[3]           D. Y. Yang, J. S. Ryoo, An investigation into the relationship between torque and load in ring rolling, Journal of Engineering for Industry, Vol. 109, No. 3, pp. 190-196, 1987.

[4]           G. Zhou, L. Hua, D. S. Qian, 3D coupled thermo-mechanical FE analysis of roll size effects on the radial–axial ring rolling process, Computational Materials Science, Vol. 50, No. 3, pp. 911-924, 2011.

[5]           L. Fausett, Fundamentals of neural networks: architectures, algorithms, and applications, Prentice-Hall, Inc. , 1994.

[6]           H. Altınkaya, I. E. I. M. Orak, Artificial neural network application for modeling the rail rolling process, Expert Systems with Applications, Vol. 41, No. 16, pp. 7135-7146, 2014.

[7]           M. Sharififar, S. A. A. A. Mousavi, Numerical study and genetic algorithm optimization of hot extrusion process to produce rectangular waveguides, Journal of Computational Applied Mechanics, Vol. 47, No. 2, pp. 129-136, 2016.

[8]           M. Moghaddam, F. Kolahan, An empirical study on statistical analysis and optimization of EDM process parameters for inconel 718 super alloy using D-optimal approach and genetic algorithm, Journal of Computational Applied Mechanics, Vol. 46, No. 2, pp. 267-277, 2015.

[9]           S. Jalilirad, M. H. Cheraghali, H. A. D. Ashtiani, Optimal Design of Shell-and-Tube Heat Exchanger Based on Particle Swarm Optimization Technique, Journal of Computational Applied Mechanics, Vol. 46, No. 1, pp. 21-29, 2014.

[10]         D. Y. Yang, J. S. Ryoo, J. C. Choi, W. Johnson, Analysis of roll torque in profile ring rolling of L-sections, In: Proceedings 18th MTDR conference, London, pp. 69-74, 1980.

[11]         G. I. Li, S. Kobayashi, Rigid-plastic finite-element analysis of plane strain rolling, Journal of Engineering for Industry, Vol. 104, No. 1, pp. 55-64, 1982.

[12]         C. C. Chen, S. Kobayashi, Rigid-plastic finite element analysis of ring compression, Applications of Numerical Methods to Forming Processes, Vol. 28, pp. 163-174, 1978.

[13]         G. R. Johnson, W. H. Cook, A constitutive model and data for metals subjected to large strains, high strain rates and high temperatures, In Proceedings of the 7th International Symposium on Ballistic, 1983.

[14]         S. P. F. C. Jaspers, H. Dautzenberg, Material behaviour in conditions similar to metal cutting: flow stress in the primary shear zone, Journal of Materials Processing Technology, Vol. 122, No. 2, pp. 322-330, 2002.

[15]         J. Deng, X. L. D. Gu, Z. Q. Yue, Structural reliability analysis for implicit  performance functions using artificial neural network, Structural Safety, Vol. 27, No. 1, pp. 25-48, 2005.

[16]         R. Kazan, M. Firat, A. E. Tiryaki, Prediction of springback in wipe-bending process of sheet metal using neural network, Materials & Design, Vol. 30, No. 2, pp. 418-423, 2009.

[17]         B. Widrow, M. A. Lher, 30 years of adaptive neural networks: perceptron, madeline and backpropagation, Proceedings of the IEEE, 1990.