Quality-related inner-phase evolution analysis and quality prediction for uneven batch processes

Abstract

In this paper, a new statistical process analysis and quality prediction method is proposed for multiphase batch processes. A two-level phase division algorithm is designed to capture and trace quality-related inner-phase evolution which in general goes through three statuses sequentially, i.e., transition, steady-phase and transition. Partial least squares (PLS), canonical correlation analysis (CCA) and qualitative trend analysis (QTA) are effectively combined to distinguish different inner-phase process statuses. Their different characteristics are then analyzed respectively for regression modeling and quality analysis. Meanwhile, the uneven-length problem of batch processes is handled in different inner-phase parts so that online quality prediction can be performed at each time. The application to the injection molding process illustrates the feasibility and performance of the proposed algorithm.