Deep learning‐based optimal tracking control of flow front position in an injection molding machine

Abstract

AbstractThe product quality of injection‐molded plastic is closely related to the injection flow velocity of molten plastics. In this article, an optimal tracking control problem for the injection flow front position arising in the filling process in the injection molding machine (IMM) is considered, and an intelligent real‐time optimal control method based on deep neural networks (DNNs) is developed for the online tracking of the flow front position to improve the efficient production process of the plastics. To this end, a highly nonlinear dynamic model describing the filling process is first proposed and then an optimal control problem is formulated. A high‐fidelity numerical optimization approach known as the Gauss pseudospectral method is used to numerically obtain the optimal state‐control solutions, which can then be saved as a huge training data set for the DNNs. The DNNs architecture is then developed and offline trained using the obtained data set to determine the best state–control relationship. As a consequence, the well‐trained DNN may be utilized to produce the matching optimum feedback action on‐board. Numerical studies are also carried out to verify the performance of our proposed approach.