A Cloud-Edge Collaborative Soft Sensing Framework for Multiperformance Indicators of Manufacturing Processes With Irregular Sampling

Abstract

In the process industry production, the online sensing of process performance is very important for the optimization and control of the manufacturing process. However, the information island is formed by long processes and multiple systems of complex production processes. The process data are characterized by high dimensional heterogeneity, nonlinearity, and strong coupling, and the offline assay of process performance is characterized by high discretization and irregular sampling period. In order to solve the above problems, a cloud-edge collaborative soft sensing framework for multiperformance indicators prediction of manufacturing processes with nonregular sampling is proposed. Also, some experiments are carried out with the actual hot strip rolling process, which realizes the joint real-time sensing of the three performance indicators of yield strength (YS), tensile strength (TS), and elongation (EL) with good accuracy.