A Quality-Relevant Sequential Phase Partition Approach for Regression Modeling and Quality Prediction Analysis in Manufacturing Processes

Competition and demand for consistent and high-quality product have spurred the development of quality prediction methods for industrial manufacturing processes. Multiplicity of phases is, in general, common nature of many batch manufacturing processes. Considering that different phases may have different effects on qualities, one of the key issues is how to partition the whole batch process into multiple phases. In the present work, an automatic quality-relevant step-wise sequential phase partition (QSSPP) algorithm is developed for phase-based regression modeling and quality prediction. It considers the time sequence of operation phases and can capture the time-varying quality prediction relationships. Using this algorithm, phases are separated in order from quality-relevant perspective, revealing different quality prediction relationships. The phase-based regression system is set up for online quality prediction and the online prediction results are quantitatively evaluated for each phase. The feasibility and performance of the proposed algorithm are illustrated by an important manufacturing process, injection molding.