Low pressure casting has the characteristics of good molding performance, high surface quality, and high production efficiency, but it also produces casting defects such as pores, cracks, shrinkage cavities, and porosity during the production process. In the low-pressure casting process of aluminum alloy sleeves, there are many factors that affect the quality of the castings, including mold structure, casting process parameters, and pouring system structure. When the structure of the casting system is determined, it is usually through adjusting process parameters to improve the forming quality and mechanical properties of the casting.
Thanks to the development of casting CAE, technicians can conduct numerical simulations of casting forming process plans and adjust process parameters by analyzing simulation results to meet the quality requirements of castings. However, due to the large number of casting process parameters, adjusting based solely on experience is often time-consuming and laborious, and it is also difficult to obtain optimal process parameter combinations, resulting in a decrease in the yield of castings.
To address the above issues, most current solutions are to design orthogonal or Taguchi experiments and obtain the optimal combination of process parameters based on mean and variance analysis. However, most of these methods only consider the impact of a single process parameter on the quality of low-pressure casting and ignore the coupling effect between each parameter. Therefore, further research is needed to optimize the process parameters of low-pressure casting.
Taking an A356 aluminum alloy sleeve as an example, experimental design was conducted based on response surface analysis method, and the pouring process was simulated using ProCAST software. The response surface experiment focuses on the preheating temperature of the upper mold, the side mold, the lower mold, and the pouring temperature. The influence of various parameters on the shrinkage volume and secondary dendrite arm spacing was analyzed, and a response surface model was constructed between the independent variable and the target quantity. Using genetic algorithm to optimize process parameters and obtain the optimal combination of process parameters, in order to provide guidance for the actual production of aluminum alloy sleeves.