An investigation of predictive control for aluminum wheel casting via a virtual process model

Abstract

An advanced control solution, specifically Model-based predictive control (MPC), has been developed for a low-pressure die casting (LPDC) process used to manufacture aluminum alloy wheels. The control solution was developed offline using a high-fidelity 3D finite element model as a virtual process on which open and closed loop trials were performed. Open loop trials were used to assess the influence of process variables and to facilitate the development of a reduced-order state-space model to predict the approximate input–output behaviour of the casting process. An MPC controller employing this state-space model was used to regulate die temperatures in the virtual process during disturbance scenarios. Two disturbances typically found in the industrial process were simulated, that is, the temperature behaviour of the molten metal in the industrial process and the length of time the die remains open after the cast wheel is ejected from the dies. These disturbances cause the die temperatures to deviate from their optimal values, which can result in defective wheels. The developed control solution improved the process performance by rejecting the simulated disturbances and maintaining the die temperatures near their optimal values.