Prediction of deformations of steel plate by artificial neural network in forming process with induction heating

다국어 초록 (Multilingual Abstract)

To control a heat source easily in the forming process of steel plate with heating, the electro-magnetic induction process has been used as a substitute of the flame heating process. However, only few studies have analyzed the deformation of a workpiece in the induction heating process by using a mathematical model. This is mainly due to the difficulty of modeling the heat flux from the inductor traveling on the conductive plate during the induction process. In

this study, the heat flux distribution over a steel plate during the induction process is first analyzed by a numerical method with the assumption that the process is in a quasi-stationary state around the inductor and also that the heat flux itself greatly depends on the temperature of the workpiece. With the heat flux, heat flow and thermo-mechanical analyses on the plate to obtain deformations during the heating process are then performed with a commercial FEM program for 34 combinations of heating parameters. An artificial neural network is proposed to build a simplified relationship between deformations and heating parameters that can be easily utilized to predict deformations of steel plate with a wide range of heating parameters in the heating process. After its architecture is optimized, the artificial neural network is trained with the deformations obtained from the FEM analyses as outputs and the related heating parameters as inputs. The predicted outputs from the neural network are compared with those of the experiments and the numerical results. They are in good agreement.

참고문헌 (Reference)

1 H. Kawaguchi, "Thermal and magnetic field analysis of induction heating problem" 161 : 193-198, 2205

2 S. C. Chen, "Simulations and verifications of induction heating on a mold plate" 31 (31): 971-980, 2004

3 B. M. Wilamowski, "S. Iplikci, O. Kaynak and M. Onder Efe" 2001

4 C.-D. Jang, "Prediction of plate bending by high-frequency induction heating" 18 (18): 226-236, 2002

5 N. N. R. Ranga Suri, "Parallel Levenberg-Marquardt-based Neural Network Training on Linux clusters- A case study" 2002

6 J. Nerg, "Numerical solution of 2D and 3D induction heating problems with non-linear material properties taken into account" 36 (36): 3119-3121,

7 M. T.Hagan, "Neural Network Design" PWS Publishing Company 1996

8 D. Nguyen, "Improving the learning speed of 2-layer Neural Networks by choosing initial values of the adaptive weights" 1990

9 L. Fausett, "Fundamentals of Neural Networks: Architectures, Algorithms, and Applications" Prentice Hall 1994

10 A. Boadi, "Designing of suitable construction of high element method" 41 (41): 4048-4050, 2005

11 K.-Y. Bae, "Derivation of simplified formulas to predict deformations of plate in steel forming process with induction heating" 48 : 1646-1652, 2008

12 V. Cingoski, "Analysis of magneto-thermal coupled problem involving moving eddy-current conductions" 32 (32): 1042-1045, 1996

13 O. Fontenla-Romero, "Accelerating the convergence speed of neural networks learning methods using least squares" 2003

14 J. G. Shin, "A User-friendly, Advanced Line heating automation for accurate plate fabrication" 19 (19): 8-15, 2003

15 K. Sadeghipour, "A Computer Aided Finite Element- Experimental Analysis of Induction Heating Process of Steel" 28 : 195-205, 1996