Robustness Assessment in the Optimization of Low-Pressure Die Casting Subject to Variations in Secondary Alloy Composition

Abstract

Porosity is a significant factor affecting the final mechanical properties in aluminum casting. Therefore, minimizing porosity by optimizing the casting parameters is of great importance. However, during normal production, some variability must be considered for these parameters, especially when using secondary alloys. Variations in alloy composition can greatly influence the solidification process, microstructure, and the product’s mechanical properties. Accordingly, achieving a robust design that accounts for secondary alloy composition variations is crucial to ensure the consistent quality and performance of the cast parts. This research uses a car wheel as a case study for a low-pressure die casting process. An optimization process is then conducted using a genetic algorithm (GA) to refine casting parameters such as heat transfer coefficient (HTC) and initial pouring temperature. Finally, the results are analyzed using the signal-to-noise ratio and the Taguchi quality loss function method to measure the robustness of the design sets. These results indicated that by conducting an optimization process and introducing noise factors as parameters, a robust design that withstand alloy variations can be achieved, and a design of simulation experiment can be established.