A heuristic approach to meet geometric tolerance in High Pressure Die Casting

Abstract In High Pressure Die Casting (HPDC), geometrical distortions usually happen during the cooling phase, due to the reduced cooling time and the high thermal gradient inside the product itself. This phenomenon affects most the thin walled products. The usual die design practice considers only the linear shrinking of the product during the cooling as a consequence of the difficult to take in account also the geometrical deformations. In this essay a simple finite element design strategy that allows the designer to improve the die shape is presented. The proposed approach uses an automatic iterative optimization technique based on a heuristic algorithm, which could be easily applied to most of the Finite Element (FE) commercial software: the basic concept of the method is simply to move the nodes defining the die surface in the opposite direction to the error due to the cooling phenomena. An automotive component has been selected as a case study: the aim was to improve the planarity tolerance of a planar surface of the casted product. Results show the efficiency of the proposed method that, despite its simplicity, is able to provide an optimal solution with a small number of iterations.

[1]  V D Tsoukalas,et al.  A study of porosity formation in pressure die casting using the Taguchi approach , 2004 .

[2]  Matthew S. Dargusch,et al.  The influence of pressure during solidification of high pressure die cast aluminium telecommunications components , 2006 .

[3]  M. Ferry,et al.  Effect of die-casting parameters on the production of high quality bulk metallic glass samples , 2006 .

[4]  Paul Xirouchakis,et al.  An intelligent system for predicting HPDC process variables in interactive environment , 2008 .

[5]  Paul W. Cleary,et al.  Short shots and industrial case studies: Understanding fluid flow and solidification in high pressure die casting , 2010 .

[6]  George-Christopher Vosniakos,et al.  Simulation-based selection of optimum pressure die-casting process parameters using neural nets and genetic algorithms , 2006 .

[7]  M. Dokainish,et al.  A survey of direct time-integration methods in computational structural dynamics—I. Explicit methods , 1989 .

[8]  Matthew S. Dargusch,et al.  A predictive model for the evolution of the thermal conductance at the casting–die interfaces in high pressure die casting , 2010 .

[9]  W. D. Griffiths A model of the interfacial heat-transfer coefficient during unidirectional solidification of an aluminum alloy , 2000 .

[10]  Nielen Stander,et al.  An Optimization Procedure For Springback Compensation Using LS-OPT , 2002 .

[11]  Saeid Nahavandi,et al.  Integrated optimization system for high pressure die casting processes , 2008 .

[12]  V. Tsoukalas Optimization of porosity formation in AlSi9Cu3 pressure die castings using genetic algorithm analysis , 2008 .

[13]  M. Bamberger,et al.  Determination of heat transfer coefficients during water cooling of metals , 1986 .

[14]  Y. Nishida,et al.  The air-gap formation process at the casting-mold interface and the heat transfer mechanism through the gap , 1986 .

[15]  K. Prabhu,et al.  Heat transfer and solidification behaviour of modified A357 alloy , 2007 .