This manuscript presents an advanced modeling methodology developed to accurately simulate the temperature field evolution in the die and wheel in an industrial low-pressure die casting (LPDC) machine employed in the production of A356 automotive wheels. The model was developed in the commercial casting simulation platform ProCAST for a production die operating under production conditions. Key elements in the development of the model included the definition of the resistance to heat transfer across the die/casting interfaces and die/water-cooling channel interfaces. To examine the robustness of the modeling methodology, the model was applied to simulate production and non-production process conditions for a die cooled by a combination of water and air-cooling (Die-A), and to a second die for a different wheel geometry (Die-B) utilizing only water cooling for production conditions. In each case, the model predictions with respect to in-die and in-wheel temperature evolution were compared to industrially derived thermocouple (TC) data, and were found to be in good agreement. Once tuned to the process conditions for Die-A operating under production conditions, no further tuning of the die/casting interface resistance was applied. Additionally, the model results, in terms of the prediction of pockets of solid encapsulated liquid, were used to compare to x-ray images of wheels. This comparison indicated that the model was able to predict clusters of porosity associated with encapsulated liquid with an equivalent radius of ~27 mm.

In this work, a plant trial was conducted on an industrial low pressure die casting (LPDC) manufacturing process for the production of aluminum alloy wheels. Various types of data have been acquired, including extensive measurements of temperature at different locations (die, wheel and cooling channels), pressure in cooling channels and size/location of shrinkage porosity in the produced wheels. Moreover, two process conditions were tested in the trial—one was the standard production process condition and the other was designed to generate shrinkage porosity in wheels by altering the die temperature. The large amount of quantitative data acquired in this study helped us to understand the key transport phenomena occurring in the process, which include: (1) a thorough picture of the evolution in temperature at a large number of discrete locations in the die and the casting; (2) the dynamic and complicated heat transfer in the cooling channels both water-on and water-off stages, associated with boiling water heat transfer. This paper (Part I) presents the results and findings obtained from the process characterization. The follow-on paper (Part II) will introduce the developed modeling methodology based on the data produced from this work.

Macrosegregation is commonly seen and has been extensively studied in large ingots in a variety of alloy systems. In comparison, this defect is rarely investigated in small aluminum shape castings. To address this shortcoming, a numerical model was developed to investigate silicon macrosegregation during the low-pressure die casting of aluminum alloy (A356) automotive wheels. The model results were compared with silicon distribution maps measured using an optical, phase area-based technique. The model of the wheel casting process was implemented within FLUENT, a commercial Computational Fluid Dynamics (CFD) software package. In the formulation adopted, liquid metal flow is driven solely by solidification shrinkage due to the variation in density between the liquid and solid phases. Buoyancy and die filling have been ignored. Additionally, the model includes Darcy flow in the two-phase mushy zone, the release of latent heat, and solute redistribution at the micro-scale using the Scheil approximation. The model was validated against temperature and segregation data taken from a commercially cast wheel and shown to be qualitatively correct in predicting trends in temperature histories and segregation. A closer inspection of the data reveals that the model is quantitatively accurate within 10–30%, depending on the location.