Manufacturing system variation reduction through feed-forward control considering model uncertainties

Today's manufacturing industry is facing greater challenges than ever. To meet the higher and stricter challenges and demands, advanced manufacturing paradigms such as flexible manufacturing and reconfigurable manufacturing are widely used by manufacturers to perform complex manufacturing operations. Complex manufacturing is characterized by a diverse product mix, various sources of disturbances, a large number of operations and stations, and the inevitable complex interactions among stations, and between processes and products. This dissertation deals with modeling and process control to enhance product quality produced in complex manufacturing processes, including multistage manufacturing processes. The successful deployment of these techniques will lead to new levels of quality and robustness in manufacturing. Fundamental research has been conducted on active control of multistage manufacturing systems. This includes three topics related to control and modeling, which are: (1) Development of feed-forward controllers for manufacturing processes: Feed-forward controllers allow deviation compensation on a part-by-part basis using programmable tools. The control actions take into consideration not only process mathematical models and in-line measurements, but also the modeling and measurement uncertainties. Simulation results show that the proposed control approach is effective in variation reduction, both for a data-driven model and for an engineering-driven model. (2) Stream of Variation (SoV) Modeling with consideration of model uncertainties: To model the variation propagation and model changes in Multistage Manufacturing Processes (MMPs) for control purposes, it is necessary for the model to capture the impact of model uncertainties that are due to the errors of incoming parts or errors arising from other process variations. This development of a modeling method considering model uncertainties enables the development of the above-mentioned control strategy. (3) Model and controllability validation in real multistage manufacturing processes: As the theoretical basis for model-based predictive controls and many other applications in multistage manufacturing, the SoV model is validated in real manufacturing processes. At the same time, the controllability in MMPs also needs to be validated in real processes. The results of experiments provide a solid theoretical basis in the SoV theory and its applications including active control.