Optimization of Squeeze Cast Process Parameters Using Taguchi and Grey Relational Analysis

Abstract The near-net shape manufacturing capabilities of squeeze casting process have greater potential to achieve smooth uniform surface and internal soundness in the cast components. In squeeze casting process, casting density and surface finish is influenced majorly by process variables. Proper control of the process variables is essential to achieve better results. Hence in the present work an attempt made using taguchi method to analyze the squeeze cast process variables such as squeeze pressure, die and pouring temperature considering at three different levels using L 9 orthogonal array. Pareto analysis of variance performed on each response to find out optimum process parameter levels and significant contribution of each individual process parameter towards surface roughness and density of LM20 alloy. Grey relation analysis used as a multi-response optimization technique to obtain the single optimal process parameter setting for both the responses surface roughness and casting density.

[1]  K. Prabhu,et al.  Modification of eutectic silicon in Al–Si alloys , 2008 .

[2]  E. Hajjari,et al.  An investigation on the microstructure and tensile properties of direct squeeze cast and gravity die cast 2024 wrought Al alloy , 2008 .

[3]  G. O. Verran,et al.  DOE applied to optimization of aluminum alloy die castings , 2008 .

[4]  F. Quadrini,et al.  Cooling rate inference in aluminum alloy squeeze casting , 2007 .

[5]  V. P. Arunachalam,et al.  Optimization of squeeze cast parameters of LM6 aluminium alloy for surface roughness using Taguchi method , 2006 .

[6]  B. Niroumand,et al.  Effects of squeeze casting parameters on the microstructure of LM13 alloy , 2009 .

[7]  P. Senthil,et al.  Optimization of squeeze casting parameters for non symmetrical AC2A aluminium alloy castings through Taguchi method , 2012 .

[8]  M. A. El-khair,et al.  Microstructure characterization and tensile properties of squeeze-cast AlSiMg alloys , 2005 .

[9]  V. P. Arunachalam,et al.  Modelling and multi objective optimization of LM24 aluminium alloy squeeze cast process parameters using genetic algorithm , 2007 .

[10]  G. P. Syrcos,et al.  Die casting process optimization using Taguchi methods , 2003 .

[11]  V. P. Arunachalam,et al.  Experimental study of squeeze casting of gunmetal , 2005 .

[12]  V. P. Arunachalam,et al.  Optimization of squeeze casting process parameters using Taguchi analysis , 2007 .

[13]  P. Senthil,et al.  Experimental Study and Squeeze Casting Process Optimization for High Quality AC2A Aluminium Alloy Castings , 2014 .

[14]  W. Xia,et al.  Experimental and numerical analysis of gas entrapment defects in plate ADC12 die castings , 2009 .

[15]  V. Hosseini,et al.  Study on the effect of cooling rate on the solidification parameters, microstructure, and mechanical properties of LM13 alloy using cooling curve thermal analysis technique , 2013 .

[16]  Babur Ozcelik,et al.  Comparison of the warpage optimization in the plastic injection molding using ANOVA, neural network model and genetic algorithm , 2006 .

[17]  Shuming Xing,et al.  Influence of technical parameters on strength and ductility of AlSi9Cu3 alloys in squeeze casting , 2013 .

[18]  K. Neailey,et al.  Macrosegregation in thin walled castings produced via the direct squeeze casting process , 2003 .

[19]  V. P. Arunachalam,et al.  Study of surface roughness in squeeze casting LM6 aluminium alloy using Taguchi method , 2007 .