DA-CBGRU-Seq2Seq Based Soft Sensor for Mechanical Properties of Hot Rolling Process

High-precision prediction of material properties is an advanced direction of hot rolled product technology in manufacturing processes. However, complex disturbance usually produces wrong or missing data in the acquisition and entry stages. The long process and the diverse operating conditions bring great challenges to constructing soft sensors for the hot rolling process. To address this problem, a new time-series soft sensor model framework is proposed for mechanical properties prediction. First, in order to process the raw dataset, the isolation forest algorithm is used to remove the outliers, and the random forest algorithm is used to fill in missing values. Second, the auxiliary variables are extracted by the combination of the XGBoost algorithm and prior mechanistic knowledge. Then, a dual-stage attention-convolutional neural network and bidirectional gated recurrent unit (DA-CBGRU)-sequence-to-sequence (Seq2Seq) time-series model based on a two-stage attention mechanism is designed for soft sensor modeling. In the encoder stage, external factors are introduced by an entity embedding layer. A combination structure of bidirectional gated recurrent units (GRUs) and convolutional layers is designed for extracting and learning the input features adaptively with the input attention mechanism. In the decoder stage, the long-term time dependence of the time series is learned by the temporal attention mechanism. Finally, the reliability of the proposed sensor model is compared and verified by the hot rolling process. The results indicate that the proposed method has an accuracy of over 95% in predicting the mechanical properties of a real hot-rolling process dataset, and has a better performance compared to other comparative soft sensor prediction methods.

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