Luping Zhao

Phase Transition Analysis Based Quality Prediction for Multi-phase Batch Processes

Batch processes are usually involved with multiple phases in the time domain and many researches on process monitoring as well as quality prediction have been done using phase information. However, few of them consider phase transitions, though they exit widely in batch processes and have non-ignorable impacts on product qualities. In the present work, a phase-based partial least squares (PLS) method utilizing transition information is proposed to give both online and offline quality predictions. First, batch processes are divided into several phases using regression parameters other than prior process knowledge. Then both steady phases and transitions which have great influences on qualities are identified as critical-to-quality phases using statistical methods. Finally, based on the analysis of different characteristics of transitions and steady phases, an integrated algorithm is developed for quality prediction. The application to an injection molding process shows the effectiveness of the proposed algorithm in comparison with the traditional MPLS method and the phase-based PLS method.

Phase-based recursive regression for quality prediction of multiphase batch processes

Batch processes have been widely applied in industry. Quality prediction plays an important role in batch process quality control. Multiple phases within a batch have correlated contributions to final qualities. So, previous phases should be considered in the quality-regression modeling for the current phase. In this paper, a new phase-based recursive statistical quality regression method is proposed for the quality prediction of multiphase batch processes. First, within each batch, main phases are obtained by a basic process analysis. Second, by analyzing phase characteristics, phases which have significant impacts on final qualities are identified as critical-to-quality phases. Then, a recursive quality regression algorithm is proposed using each quality residual of the regression models built in those critical-to-quality phases. Besides, phase characteristics are represented by the average trajectories of process variables within each phase. Quality prediction is performed at each sample point for online prediction. The application to a typical multiphase batch process, injection molding process, illustrates the feasibility and performance of the proposed algorithm.

Multimode analysis and online monitoring for injection molding processes

Injection molding process is a typical batch processing technique to manufacture plastic products. In such process, plastic particles are heated to melt, injected to a mold with a certain configuration, and cooled down to solid state. In recent years, because of the more pressing market demand for modern multi-species, multi-standard and high-quality products, more attention have been paid on batch processes which can produce small-quantity, high-added-value products. In order to improve the safety of multimode production process, inter-mode analysis is an urgent need to establish monitoring for multimode production process. Besides inherent process variation along the time direction within each batch, multimode characteristic along the batch direction widely exists in injection molding processes. Different operation modes along the batch direction may not be well captured by a single model. In this work, multiple modes are modeled separately for online process monitoring along the batch direction. First, after a batch cycle is divided into multiple phases, normality test is conducted to analyze the data characteristic. Then, ICA-PCA models are constructed for each mode within each phase according to their different characteristic considering both the Gaussian and non-Gaussian information of the process data. Then online monitoring model is properly chosen by identifying which mode the new sample point belongs to. The application to a real injection molding process illustrates the feasibility and performance of the proposed algorithm.

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.