Open Access Open Access  Restricted Access Subscription or Fee Access

Process Parameters Optimization for Isothermal Forging of Ti-6Al-4V Alloy using Taguchi Method and Artificial Neural Network

Rajkumar Ohdar, Sankar Behera, Israr Equbal, Azhar Equbal

Abstract


Enabling and developing the required „Process‟ efficiency in an Input-Process-Output System, is bound to provide effective solutions for today‟s need for maximized production especially in dwindling Input conditions. Process optimization is a significant and contributing step towards such process efficiency as it paves way for improved overall productivity. The present work involves, an approach using soft computing paradigm for the process parameter optimization of multiple input single output isothermal forging of Ti-6Al-4V alloy. In this paper a combination of Taguchi‟s L27 Orthogonal Array (OA-L27) along with back- propagation artificial neural network and engineering optimization concepts to determine the optimal process parameter settings of forging temperature, strain rate and strain for the isothermal forging. The optimum solution is valid in the ranges of forging process parameters that were used for training the artificial neural network.

Keywords


Isothermal Forging, Taguchi Method, Artificial Neural Network, Flow Stress, Ti-alloy

Full Text:

PDF

References


K.P. Rao, Y.K.D.V. Prasad, neural network approach to flow stress evaluation in hot deformation, Journal of Materials Processing Technology 53 (1995) 552–566.

Nho-Kwang Park, Jong-Taek Yeom, Young-Sang Na, Characterization of deformation stability in hot forging of conventional Ti-6Al-4V using

processing maps, Journal of Materials Processing Technology , 130-131 (2002) 540-545.

Yu.-W Chen, “Titanium alloy and its application in aerospace industries” Rare metal (1996)297-300.

Z. M. Hu, T. A. Dean, Aspect Of Forging Of Titanium Alloys And The Production Of Blade Forms, Journal of Materials Processing Technology, 111 (2001) 10-19.

James D. Destefani, Bailey Control Co., Introduction to Titanium and Titanium Alloys, Properties And Selection: Non Ferrous Alloys & Special Purpose Materials, Metal Hand Book, Vol-2, Edition-10, Asm 1990.

E. W. Colings, the Physical Metallurgy Of Titanium Alloys, ASM 1984 P2.

P. J. Ross, Taguchi Techniques for quality Engineering, McGraw-Hill, New York, 1988.

Y. S. Zu, S. T. Lin, Optimizing the mechanical properties of injection molded W-4.9% Ni-2.1% Fe in debinding, Journal of Materials Processing Technology, 71 (1997) 337-342.

S.V.S.N. Murty and B. N. Rao (2000), on the flow optimization concepts in the processing maps of titanium alloy Ti-24Al-20Nb, Journal of materials processing technology, 104, 103-109.

Peace Glen Stuart. Taguchi methods: a hand on approach, 1993 (Addison Wesley, New York).

MATLAB neural network toolbox, The MathWorks Inc., Prentice- Hall, Englewood Cliffs; 1998.

LiMin Fu, Neural Network learning, Neural Networks in computer intelligence, Tata McGraw-Hill Edition 2003,p 80-90.

Casalino G, De Filippis LAC, Ludovico A. A technical note on the mechanical and physical characterization of selective laser sintered sand for rapid casting. J Mater Process Technol 2005;166(1):1–8




DOI: http://dx.doi.org/10.36039/AA112011003

Refbacks

  • There are currently no refbacks.


Creative Commons License
This work is licensed under a Creative Commons Attribution 3.0 License.