Hybrid forming process of AA 6108 T4 thin sheets: Modelling by neural network solutions

Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture

Volumn 223, Issue 5, 2009, Pages 535-545

  Barletta, M ^a   Gisario, A ^a   Guarino, S ^a,b  

^a SAPIENZA UNIVERSITY OF ROME   (Italy)

^b UNIVERSITY OF ROME TOR VERGATA   (Italy)

Author keywords

aluminium alloy; Bending process; Diode laser; Neural network model

Indexed keywords

ALUMINIUM ALLOY; ARTIFICIAL INTELLIGENCE TECHNIQUES; BENDING PROCESS; DIODE LASER; ELASTIC STRAIN ENERGY; FORMED MATERIALS; FORMING PROCESS; HIGH-POWER DIODE LASERS; LASER SOURCES; LOCAL HEATING; MECHANICAL CONTACT; NETWORK SOLUTIONS; NEURAL NETWORK MODEL; NONLINEAR DEFORMATIONS; OPERATING PARAMETERS; PROCESS PARAMETERS; RESIDUAL DEFLECTION; SPRING-BACK; THIN SHEET; TRAINING ALGORITHMS;

ALUMINUM; ALUMINUM ALLOYS; DEFORMATION; DIODES; HEATING; LASERS; NEURAL NETWORKS; SHEET METAL; WIRELESS TELECOMMUNICATION SYSTEMS;

METAL FORMING;

EID: 67449151889     PISSN: 09544054     EISSN: None     Source Type: Journal    
DOI: 10.1243/09544054JEM1218     Document Type: Conference Paper

References (20)

1. Dieter, G. E. Mechanical metallurgy, third edition, 1998 (McGraw-Hill, London).
2. Narasimhan, N. and Lovell, M. Predicting springback in sheet metal forming: an explicit to implicit sequential solution procedure. Finite Elem. in Anal. andDes., 1999, 33, 29-42.
3. Lan, F., Chen, J., and Lin, J. A method of constructing smooth tool surfaces for FE prediction of springback in sheet metal forming. J. Mater. Process. Technol., 2006, 177, 382-385.
4. Cho, J. R., Moon, S. J., Moon, Y. H., and Kang, S. S. Finite element investigation on spring-back characteristics in sheet metal U-bending process. J. Mater. Process. Technol., 2003, 141(1), 109-116.
5. Geng, L. M. and Wagoner, R. H. Role of plastic aniso-tropy and its evolution on springback. Int. J. Mech. Sci.2002, 44, 123-148.
6. Li, K. P., Carden, W. P., and Wagoner, R. H. Simulation of springback. Int. J. Mech. Set, 2002, 44, 103-122.
7. Liu, Y. C. The effect of restraining force on shape deviation in flanged channels. ASME J. Engng Mater. Technol., 1988, 110,389.
8. Sunseri, M., Cao, J., Karafillis, A. P., and Boyce, M. C. Accommodation of springback error in channel forming using active binder force control: numerical simulations and experiments. ASME J. Engng Mater. Technol., 1996, 118, 426.
9. Moon, Y. H., Kang, S. S., Cho, J. R., and Kim, T. G. Effect of tool temperature on the reduction of the springback of aluminium sheets. J. Mater. Process. Technol., 2003, 132, 365-368.
10. Shi, Y., Yao, Z., Shen, H., and Hu, J. Research on the mechanisms of laser forming for the metal plate. Int. J. Mach. Tools Mf., 2006, 46, 1689-1697.
11. Costa Santos, E., Shiomi, M., Osakada, K., and Laoui, T. Rapid manufacturing of metal components by laser forming.Int. J. Mach. Tools Mf., 2006, 46, 1459-1468.
12. Shen, H., Shi, Y., Yao, Z., and Hu, J. An analytical model for estimating deformation in laser forming. Comput. Mater. Sci., 2006, 37, 593-598.
13. 3 coated and uncoated aluminium thin sheets. Surf. Coat. Technol., 2006, 201(3-4), 660-673.
14. Geiger, M., Merklein, M., and Pitz, M.Laser and forming technologyan idea and the way of implementation. J. Mater. Process. Technol., 2004, 151(1/3), 3-11.
15. Schuöcker, D. Mathematical modeling of laser-assisted deep drawing. J. Mater. Process. Technol., 2001, 115(1), 104-107.
16. Montgomery, D. C. Controllo Statistico della qualitd, 2006 (McGraw Hill, Milan).
17. Liu, W., Liu, Q., Ruan, F., Liang, Z., and Qiu, H. Springback prediction for sheet metal forming based on GA-ANN technology. J. Mater. Process. Technol., 2007, 12, 227-231.
18. Ko, D.-C., Kim, D.-H., and Kim, B.-M. Application of artificial neural network and Taguchi method to preform design in metal forming considering workability. Int. J. Mach. Tools Mf., 1999, 39, 771-785.
19. Cheng, P. J. and Lin, S. C. Using neural networks to predict bending angle of sheet metal formed by laser. Int.J. Mach. Tools Mf., 2000, 40, 1185-1197.
20. Jeng-Jeng, J.-Y., Maub, T.-F., and Leuc, S.-M. Prediction of laser butt joint welding parameters using back propagation and learning vector quantization networks. J. Mater. Process. Technol., 2000, 99, 207-218.