Artificial neural network is implemented to predict the required load and torque in T-section profile ring rolling process for the first time in this study. Moreover, an optimal condition of T-section profile ring rolling process for specific limit of input factor is acquired using genetic algorithm technique. Various three-dimensional finite element simulations are carried out for different collections of process variables to obtain initial data for training and validation of the neural network. Besides, the finite element model is verified via comparison with the experimental results of the other investigators. The back-propagation algorithm is utilized to develop Levenberg–Marquardt feed-forward network and the optimum architecture is achieved by estimating the performance considering different number of hidden layers and neurons. It is concluded that results of artificial neural network predictions have an appropriate conformity with those ones from simulation and experiments. Moreover, a reasonable accuracy is obtained from the implemented model by which the prediction of ring rolling load and torque in different conditions can be achieved.
HahnYHYangD. UBET analysis of roll torque and profile formation during the profile ring-rolling of rings having rectangular protrusions. J Mater Process Technol1991; 26: 267–280.
2.
RyooJSYangDYJohnsonW. The influence of process parameters on torque and load in ring rolling. J Mech Working Technol1986; 12: 307–321.
3.
ParviziAAbriniaK. A two dimensional Upper Bound Analysis of the ring rolling process with experimental and FEM verifications. Int J Mech Sci2014; 79: 176–181.
4.
YangDYRyooJS. An investigation into the relationship between torque and load in ring rolling. J Eng Ind1987; 109: 190–196.
5.
HuaLZhaoZZ. The extremum parameters in ring rolling. J Mater Process Technol1997; 69: 273–276.
6.
DaveyKWardMJ. A practical method for finite element ring rolling simulation using the ALE flow formulation. Int J Mech Sci2002; 44: 165–190.
7.
ZhouGHuaLQianD. 3D coupled thermo-mechanical FE analysis of roll size effects on the radial–axial ring rolling process. Comput Mater Sci2011; 50: 911–924.
8.
PandeyCMahapatraMMKumarP, et al.Effect of weld consumable conditioning on the diffusible hydrogen and subsequent residual stress and flexural strength of multipass welded P91 steels. Metall Mater Trans B2018; 49: 2881–2895.
9.
PandeyCMahapatraMMKumarP, et al.comparative study of transverse shrinkage stresses and residual stresses in P91 welded pipe including plasticity error. Arch Civil Mech Eng2018; 18: 1000–1011.
10.
YangDYKimKHHawkyardJB. Simulation of T-section profile ring rolling by the 3D rigid-plastic finite element method. Int J Mech Sci1991; 33: 541–550.
11.
VishveshaAPandeyCMahapatraMM, et al.On the estimation and control of welding distortion of guide blade carrier for a 660 MW turbine by using inherent strain method. Int J Steel Struct2017; 17: 53–63.
12.
Fausett LV and Fausett L. Fundamentals of neural networks: architectures, algorithms, and applications. Englewood Cliffs, NJ: Prentice-Hall Inc., 1994, p.461.
13.
GiriAPandeyCMahapatraM. Achieving optimized tungsten inert gas butt welding conditions of thin cold rolled steel sheets by response surface methodology and artificial neural networks. Proc IMechE, Part E: J Process Mechanical Engineering2018; 232: 459–470.
14.
BagheripoorMBisadiH. Application of artificial neural networks for the prediction of roll force and roll torque in hot strip rolling process. Appl Math Model2013; 37: 4593–4607.
15.
AltınkayaHOrakİMEsenİ. Artificial neural network application for modeling the rail rolling process. Expert Syst Appl2014; 41: 7135–7146.
16.
GudurPPDixitUS. A neural network-assisted finite element analysis of cold flat rolling. Eng Appl Artif Intell2008; 21: 43–52.
17.
MoussaouiASelaimiaYAbbassiHA. Hybrid hot strip rolling force prediction using a Bayesian trained artificial neural network and analytical models. Am J Appl Sci2006; 3: 1885–1889.
18.
AntónioCACDouradoNM. Metal-forming process optimization by inverse evolutionary search. J Mater Process Technol2002; 121: 403–413.
19.
WeiLYuyingY. Multi-objective optimization of sheet metal forming process using Pareto-based genetic algorithm. J Mater Process Technol2008; 208: 499–506.
20.
ChungJSHwangS. Application of a genetic algorithm to process optimal design in non-isothermal metal forming. J Mater Process Technol1998; 80: 136–143.
21.
FanMHuJCaoR, et al.Modeling and prediction of copper removal from aqueous solutions by nZVI/rGO magnetic nanocomposites using ANN-GA and ANN-PSO. Scient Rep2017; 7: 18040.
22.
CaoRFanMHuJ, et al.Artificial intelligence based optimization for the Se(IV) removal from aqueous solution by reduced graphene oxide-supported nanoscale zero-valent iron composites. Materials2018; 11: 428.
23.
CaoRFanMHuJ, et al.Optimizing low-concentration mercury removal from aqueous solutions by reduced graphene oxide-supported Fe3O4 composites with the aid of an artificial neural network and genetic algorithm. Materials2017; 10: 1279.
24.
GuoLYangHZhanM. Research on plastic deformation behavior in cold ring rolling by FEM numerical simulation. Model Simul Mater Sci Eng2005; 13: 1029.
25.
KimNMachidaSKobayashiS. Ring rolling process simulation by the three dimensional finite element method. Int J Mach Tools Manuf1990; 30: 569–577.
26.
MamalisAGJohnsonWHawkyardJD. Pressure distribution, roll force and torque in cold ring rolling. J Mech Eng Sci1976; 18: 196–209.
27.
YangYYLinkensDATalamantes-SilvaJ. Roll load prediction-data collection, analysis and neural network modeling. J Mater Process Technol2004; 152: 304–315.
28.
HaganMTMenhajMB. Training feedforward networks with the Marquardt algorithm. IEEE Trans Neural Netw1994; 5: 989–993.
29.
ChatterjeeSBhattacherjeeA. Genetic algorithms for feature selection of image analysis-based quality monitoring model: an application to an iron mine. Eng Appl Artif Intell2011; 24: 786–795.
30.
WhitleyD. A genetic algorithm tutorial. Stat Comput1994; 4: 65–85.
