| Manufacturing industries are facing major challenges in terms of improving product quality and increasing throughput while sustaining production costs to acceptable levels. Product-oriented processes, both legacy and new, are poorly monitored and controlled on the basis of distributed loop controllers that are aiming to maintain critical process variables within acceptable bounds. Thus, poor quality product results when such processes are subjected to large disturbances---operational failures, environmental changes, and changes in loading conditions. In this research, product quality modeling and control based on a vision inspection methodology is proposed to improve product quality and increase productivity.; The main contributions of this research are twofold. First, this research introduces a product quality modeling methodology that combines both physical-based modeling and data-driven modeling. The quality model is the link between information coming from the inspection of product features and the specification of process control strategies. It is essential to control and optimize the process. Second, a novel approach to improve the product quality and productivity based on vision inspection, which is developed as part of a hybrid, hierarchical architecture, is the developed. The mid-level control module in this architecture, which is the focus of the work presented here, takes advantage of baking product quality indicators and oven parameter measurements to optimize zone temperature so that the "best" product quality is achieved even in the presence of disturbances. The result shows that the feed-forward combined with feedback controller strategy in mid-level control module can greatly improve the product quality. |
