Controlling and eliminating defects, such as macro-porosity, in die casting processes is an on-going challenge for manufacturers. Current strategies for eliminating defects focus on the execution of a pre-set casting cycle, die structure design or the combination of both. 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, validated by comparison with experimental data, has been developed to predict the evolution of temperatures and the volume of liquid encapsulation in an experimental casting process. A virtual process, made up of the heat transfer model and a wrapper script for communication, has been employed to simulate the continuous operation of the real process. A stochastic state-space model, based on data from measurements and the virtual process, has been developed to provide a reliable representation of this virtual process. The parameters of the deterministic portion result from system identification of the virtual process, whereas the parameters of the stochastic portion arise from the analysis and comparison of measurement data with virtual process data. The resulting state-space model, which can be extended to a multi-input multi-output model, will facilitate the design of a model-based controller for this process.
A Hybrid Nonlinear Forecasting Strategy for Short-Term Wind Speed
