The objective of this research is to identify a dynamic model that describes the temperature distribution in a die using a neural network (NN) approach. By using data sets obtained from a finite element analysis (FEA) of the thermal dynamics of a die and applying NN off-line and on-line learning algorithms, the die model is identified. This identification approach has been conducted assuming fully measurable and partially measurable states. For the latter, a NN based adaptive observer is employed to estimate unmeasurable states. It is shown that the complex behavior of the die system with cooling channels can be accurately identified in both cases of fully and partially measurable states.
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