As modern manufacturing industries become more sophisticated, it is common to find a production process involving multiple stages of production such as those found in pharmaceutical manufacturing, the chemical industry and in semiconductor and auto manufacturing. Three types of correlations in the data streams (among stages, among quality characteristics, and over time) in a multistage manufacturing process introduce significant challenges in variation modeling, analysis, and control. This project aims at developing efficient methodologies for the monitoring, control and optimization of autocorrelated multistage processes in order to improve the quality of the process output. This is a challenging problem due to the complexity of multistage processes and autocorrelations of observations that make the relationship between the output and input variables extremely complicated. This will be investigated through novel models of the propagation of variable means and variances to subsequent stages, using dynamic state space models that enable the identification of the variation source propagation and monitoring/diagnosis of the processes. The methodologies will be developed and validated in collaboration with industry partners.
The research contributes to the science base of methods and algorithms to minimize the propagation of variations and quickly detect change points in autocorrelated multistage manufacturing processes. Successful completion of this project will provide online monitoring and diagnosis methods for detecting abnormal behaviors of multistage processes. The results will be broadly applicable in a variety of industries to improve the overall quality and productivity of production systems. Broader impacts will be also generated through new curriculum modules, online software toolkits for implementation, and involving underrepresented undergraduate and graduate students in research experience programs to enhance the human resource talent for U.S. industry.
