Property Prediction Using Hierarchical Regression Model Based on Calibration Original Research Article

Redundant information and inaccurate model will greatly affect the precision of quality prediction. A multiphase orthogonal signal correction modeling and hierarchical statistical analysis strategy are developed for the improvement of process understanding and quality prediction. Bidirectional orthogonal signal correction is used to remove the structured noise in both X and Y, which does not contribute to prediction model. The corresponding loading vectors provide good interpretation of the covariant part in X and Y. According to background, hierarchical PLS (Hi-PLS) is used to build regression model of process variables and property variables. This blocking leads to two model levels: the lower level shows the relationship of variables in each annealing furnace using hierarchical PLS based on bidirectional orthogonal signal correction, and the upper level reflects the relationship of annealing furnaces. With analysis of continuous annealing line data, the production precisions of hardness and elongation are improved by comparison of previous method. Result demonstrates the efficiency of the proposed algorithm for better process understanding X and property interpretation Y.