Process modeling by integrating quantitative and qualitative information using a deep embedding network and its application to an extrusion process. (July 2022) - Wu, Haibin; Lo, Yu-Han; Zhou, Le; Yao, Yuan
Abstract: In the big data era, small data problems still exist in many industrial sectors. Taking the high-value process industries as an example, a large number of materials and processing methods are often tested at the design stage. However, only a small amount of data can be collected for each material-process combination, which poses a serious challenge to data-driven process modeling. There is a great necessity to integrate the small data measured in different tasks and build the process model by sharing the information. In this work, a deep embedding neural network is proposed to extract the qualitative task information for process modeling. Specifically, an autoencoder is used to learn embeddings which are combined with the quantitative process conditions as the inputs of a feed-forward neural network to produce the final predictions. The feasibility, including interpretability and prediction accuracy, of the developed method is illustrated with an extrusion process. Highlights: A deep embedding network is proposed for process modeling based on small data. Both quantitative and qualitative process information is integrated in the model. The embeddings have a clear physical meaning. The feasibility of the model is illustrated with a twin-screw extrusion simulation.