Design of Experiments (DOE) Based Automatic Process Control (APC): A Methodology for Process Variation Reduction Beyond Robust Parameter Design

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This project focuses on the development of automatic process control (APC) methodologies based on Design of Experiments (DOE) regression models and real-time measurement or estimation of noise factors. The central idea of the proposed research is to develop a methodology to achieve automatic process control by integrating the disciplines of DOE, SPC, and control and estimation theory. Various fundamental issues will be studied, which include: (1) a new classification of controllable factors and noise factors applicable to serve as feedback information for APC; (2) test design and analysis of DOE for process modeling with consideration of system identifiability for the control purpose; (3) cautious control strategy with consideration of uncertainties in DOE regression models and noise factor estimation; and (4) on-line DOE model updating and adaptive control with supervision.The success of the research will lead to a new scientific basis and practical tools for designing and implementing APC in complex manufacturing processes. The research expands the existing theory in each of the three well-developed disciplines of DOE, SPC, and APC to form a new "DOE-based APC" methodology. This new methodology, in conjunction with robust design and SPC, will provide more effective techniques for a broad range of manufacturing processes. Examples of such processes include stamping, forging, semiconductor, and composite material manufacturing processes.