Optimization of an aluminum profile extrusion process based on Taguchi’s method with S/N analysis

Taguchi’s design of experiment and numerical simulation were applied in the optimization of an aluminum profile extrusion process. By means of HyperXtrude, the extrusion process was simulated and the effects of process parameters on the uniformity of metal flow and on the extrusion force were investigated with the signal to noise ratio and the analysis of variance. Through analysis, the optimum combination of process parameters for uniform flow velocity distribution was obtained, with the billet diameter of 170 mm, ram speed of 2.2 mm/s, die temperature of 465°C, billet preheated temperature of 480°C, and container temperature of 425°C. Compared with the initial process parameters, the velocity relative difference in the cross-section of extrudate was decreased from 2.81% to 1.39%. In the same way, the optimum process parameters for minimum required extrusion force were gained, with the billet diameter of 165 mm, ram speed of 0.4 mm/s, die temperature of 475°C, billet preheated temperature of 495°C, and container temperature of 445°C. A 24.7% decrease of required extrusion force with optimum process parameters was realized. Through the optimization analysis in this study, the extrusion performance has been greatly improved. Finally, the numerical results were validated by practical experiments, and the comparison showed that the optimization strategy developed in this work could provide the effective guidance for practical production.

[1]  An analytical solution for the spread extrusion of shaped sections , 2009 .

[2]  Boris Štok,et al.  Tool design optimization in extrusion processes , 1998 .

[3]  S. M. Hwang,et al.  Die shape optimal design in cold and hot extrusion , 2003 .

[4]  Y. S. Tarng,et al.  Optimization of the electrical discharge machining process based on the Taguchi method with fuzzy logics , 2000 .

[5]  Durmus Karayel.  Simulation of Direct Extrusion Process and Optimal Design of Technological Parameters Using FEM and Artificial Neural Network , 2008 .

[6]  Nadir Yayla,et al.  The investigation of model selection criteria in artificial neural networks by the Taguchi method , 2007 .

[7]  Der-An Wang,et al.  Optimization of machining parameters in magnetic force assisted EDM based on Taguchi method , 2009 .

[8]  Genichi Taguchi.  System Of Experimental Design: Engineering Methods To Optimize Quality And Minimize Costs , 1987 .

[9]  Milan Jurković,et al.  Optimization of extrusion force prediction model using different techniques , 2006 .

[10]  Zou Lin,et al.  Optimization of die profile for improving die life in the hot extrusion process , 2003 .

[11]  Xueyu Ruan,et al.  Finite volume simulation and mould optimization of aluminum profile extrusion , 2007 .

[12]  K. Hans Raj,et al.  Modeling of manufacturing processes with ANNs for intelligent manufacturing , 2000 .

[13]  Naz Chaibakhsh,et al.  Optimization of lipase-catalyzed synthesis of xylitol ester by Taguchi robust design method. , 2010 .

[14]  S. S. Hajimirsadeghi,et al.  Synthesis of silver chromate nanoparticles: Parameter optimization using Taguchi design , 2010 .

[15]  Vincenzo Tagliaferri,et al.  Artificial neural networks to optimize the extrusion of an aluminium alloy , 2010, J. Intell. Manuf..

[16]  Patrick Ulysse.  Optimal extrusion die design to achieve flow balance , 1999 .