Quality metrics prediction in process manufacturing based on CNN-LSTM transfer learning algorithm

The prediction of production process quality indicators plays an important role in product quality and production scheduling in process industries. In order to exploit the effective information contained in the massive process data, improve the prediction accuracy of production process quality indicators and apply to the changes of processing conditions, a hybrid model quality indicator migration learning prediction method based on convolutional network (CNN) and long-short-term memory (LSTM) is proposed. Massive amounts of historical process data, operational data and date data were constructed into a continuous feature matrix with a time-sliding window. The feature vectors are first extracted using CNN, and the feature vectors are constructed in a time-series sequence and used as input data for the LSTM network. Then the LSTM network is used for quality index prediction. In this process, migration learning strategy is introduced, which reduced the training time while ensuring the training accuracy. Finally, the correctness and effectiveness of the proposed method is verified by using the process data of a tobacco factory microtobacco cutting test line as a case object.