Figure 29. Optimization process of riser optimization for a steel casting. The optimization code evaluates the current shrinkage distribution and recognizes, initially, the increased feeding need. It might sometimes provide an overly conservative solution (lower right), but usually adjust for a final best compromise of riser volume and casting quality. (lower left)
Figure Sources
/1/ with friendly permission of DANA Spicer, Argentina /2/ with friendly permission of Voith Paper, Brazil /3/ with friendly permission of United Technologie, P.R. China
/4/ with friendly permission of vonRoll Casting, Switzerland /5/ with friendly permission of PT Kayaba, Indonesia /6/ with friendly permission of Heidelberger Druckmaschinen, Amstetten, Germany
/7/ with friendly permission of Volkswagen AG, Kassel, Germany
/8/ with friendly permission of Coupe Foundries, Great Britain /9/ with friendly permission of Ford Forschungszentrum, Aachen and Eisenwerk Brühl, Germany
/10/ with friendly permission of BMW, Landhut, Germany /11/ with friendly permission of Ford Forschungszentrum, Aachen, Germany
REFERENCES
1. Hansen P.N., Sahm P.R.,“A 3-D Geometric Modeler- Implicxit FDM Solver Package for Simulation of Shaped Casting Solidification”, “Modelling of Casting and Welding Process II”, Metall. Soc. AIME (1984), New England College Henniker, New Hampshire, pp 243-247 (1983).
2. Sahm P.R., Hansen P.N., Numerical Simulation and Modelling of Casting and Solidification Processes for Foundry and Cast-House, CIATF (1984).
3. Flender E., Hansen P.N., Sahm P.R., “Rechnerisches Simulieren und Modellieren des Warmrißverhaltens warmfester Stahlgußsorten bei der Erstarrung”, Giessereiforschung 39, Heft 4, pp 137-149 (1987).
22
Figure 30. Multiple design variations of runners for a diecasting, which were evaluated by an autonomous optimization run. These are 6 designs out of 257 actually calculated versions. Optimization goal was to avoid a loss of contact between the melt and the runner surface. The shot chamber location and the ingate angle at the casting were fixed. Otherwise, total freedom was given to the optimization tool to find a solution. The colored areas depict undesired areas of contact loss, which lead to turbulence and air entrapment. The final and best variation (lower right) shows no critical areas. (G. Hartmann und R. Seefeldt, 2004)
4. Sturm J.C., Schäfer W., Sahm P.R., Modelling the Mold Filling and Solidification of a Steel Hammer Casting by Use of the Computer Aided Solidification Technologies (CASTS) Software System, Modeling and Control of Casting and Welding Processes IV, S. 845 , Herausgeber A.F. Giamei and G.J. Abbaschian, verlegt bei TMS (1988).
5. Egner-Walter: “Berechnung der Entstehung von Spannungen beim Gießen” Hoppenstedt, Gussprodukte `99 (1999).
6. Svensson I.L. and Wessén M.: “Foundry of Cast Irons: Processing and Simulation, Numerical Simulation of Foundry Processes”, pp 87-145 (2001).
7. Hartmann G. and Seefeldt R., “Die zweite Generation von Simulations werkzeugen: Praktische Anwendung der rechnerischen Optimierung im Druckguss”, Giesserei, Nr.2/2004, pp 38-42 (2004).
8. Sturm J.C., “Stand der Simulation für Gusseisen”, Giesserei 91, Nr. 6, Special Simulation von Giessereiprozessen p 4 (2004).
9. Hartmann G., Egner-Walter A., Dannbauer H., Simulation of Local Properties of Metal Cast Engine and Suspension Parts, Virtual Product Creation 2004, Konferenz (2004).
10. Sturm J.C.,Vorhersage lokaler Eigenschaften von Gussteilen im Motorenbau“, VDI Fachtagung Gießen im Motorenbau, Magdeburg (2005).
11. Hattel J. (Herausgeber), Fundamentals of Numerical Modelling of Casting Processes, Polyteknisk Forlag (2005).
12. Carlson K.D., Beckermann C., “Modeling of Reoxidation Inclusion Formation During Filling of Steel Castings,” in Defect Formation, Detection, and Elimination During Casting, Welding, and
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