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An internal dynamics approach to predicting batch-end product quality in plastic injection molding using Recurrent Neural Networks

Estimating a dynamic model for the task of batch end quality prediction from process measurements with only a single quality measurement after completion of the batch is a difficult modeling task. It is common practice to circumvent building a dynamic model altogether byinstead using a static model approach of some kind. These static approaches, i.e. Multivariate Statistical Process Control (MSPC) or mapping features extracted from process measurements to the quality measurement, work in general very well. Nevertheless they neglect some of the information present in the data and come with some drawbacks, i.e. the necessity for all batches having equal length in the case of MSPC methods. The purpose of this paper is therefore to demonstrate how an internal dynamics model approach, more specifically a Recurrent Neural Network, can be used to estimate a dynamic model for batch end quality prediction from process measurements. The resulting model may be used for in-batch or batch-to-batch optimization depending on the real-time requirements of the respective process. The proposed model approach is applied to a plastic injection molding (PIM) process, which is a switched system, and compared to static model approaches that are commonly employed in the PIM community.