Process adjustment techniques based on the feedback control principle have become popular among quality control researchers and practitioners, due to the recent interest on integrating Statistical Process Control (SPC) and Engineering Process Control (EPC) techniques. This thesis focuses on studying sequential adjustment methods, closely related to well-known Stochastic Approximation procedures, for the purpose of quality control of a short-run manufacturing process.; First, the problem of adjusting a machine that starts production after a defective setup operation is considered. A general solution based on a Kalman Filter estimator is presented. This solution unifies some well-known process adjustment rules, and is a particular case of Linear Quadratic (LQ) control methods. In essence, this solution calls for a sequential adjustment strategy which recursively calculates the value of an adjustable variable according to the prior knowledge of this variable and the most recent observation from the process.; Next, the integration of sequential adjustments with SPC control charts are investigated for controlling an abrupt step-type process disturbance on a manufacturing process. The performance of this type of integrated methods depends on the sensitivity of the control chart to detect shifts in the process mean, on the accuracy of the initial estimate of shift size, and on the number of sequential adjustments that are made. It is found that sequential adjustments are superior to single adjustment strategies for almost all types of process shifts and shift sizes considered.; If there are different costs associated with a higher-than-target quality characteristic compared to a lower-than-target quality characteristic, that is, an asymmetric cost function, the adjustment rule needs to be modified to avoid the quality characteristic falling into the higher cost side. For this case, a sequential adjustment rule with an additional bias term is proposed.; Finally, methods for identifying and fine-tuning a manufacturing system operating in closed-loop are studied. When a process is operated under a linear feedback control rule, the cross-correlation function between the process input and output has no information on the process transfer function, and open-loop system identification techniques cannot be used. In this research, it is shown that under certain general assumptions on the controller and process disturbance structure, it is possible to identify the process disturbance models from data obtained under closed-loop operation. (Abstract shortened by UMI.)
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