Nonlinear identification for control of low pressure die casting

Controlling and eliminating defects, such as macro-porosity, in die casting processes is an on-going challenge for manufacturers. Current strategies focus on the execution of a pre-set casting cycle and die structure design. To respond to process variability and mitigate its negative effects, advanced process control methodologies may be employed to dynamically adjust the operational parameters of the process. In this work, a finite element heat transfer model has been developed to predict the evolution of temperatures and the area of liquid encapsulation in a low pressure die casting process. The model was validated by comparison to plant trial data. A virtual process has been developed based on the model to simulate the continuous operation of the real process. A nonlinear state-space model, based on data from the virtual process, has been developed to provide a reliable representation of this virtual process. The control variables-driven portion exhibits linear dynamic behavior with nonlinear static gain, whereas the feed forward-driven portion is based on a linear function defined by system identification on the virtual process. The resulting MIMO state-space model will facilitate the design of a model-based controller for this process.