A Novel Bidirectional Gated Recurrent Unit-Based Soft Sensor Modeling Framework for Quality Prediction in Manufacturing Processes
Liang Ma, Mengwei Wang, Kaixiang Peng
Abstract
Quality prediction is very important for improving the accuracy of quality control and the stability of product quality in manufacturing processes. However, the complex time series with high dimension, nonlinearity, and dynamics brings great challenges to the traditional quality prediction methods. To address this issue, a new soft sensor modeling framework is proposed for quality prediction. Specifically, the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${k}$ </tex-math></inline-formula> -nearest neighbor mutual information is first used to mine the inherent relationship between process and quality variables for dimension reduction and variable selection. Then, a bidirectional gated recurrent unit structure is designed for dynamic, nonlinear soft sensor modeling, where the historical and future information inside industrial time series and relevant features have been fully used for quality prediction, and the backpropagation learning algorithm is given in detail. Finally, a typical manufacturing process, the hot rolling process, is used for verification, and the simulation and comparison results show that the new method is able to predict the final product quality with higher accuracy and efficiency.