SBIR Phase I: Alpha Sigma Pi - A Method for Confident, Robust, and Optimal Process Control

This Small Business Innovation Research (SBIR) Phase I project proposes a dynamic and quantitative decision support system to assist manufacturers in understanding and optimizing complex processes with multiple process settings and multiple quality requirements. The intellectual merit of the proposed research stems from the analytical solution of the feasibility with the Extensive Simplex Method. Using a constraint based approach, this representation provides the global feasibility and the local flexibility of the process given the behavioral linkages between the process settings and the quality requirements. Following these feasibility maps or "process windows", the process engineer can characterize and understand the behavior of manufacturing process, evaluate the feasibility of individual process conditions, and discover potential improvements in any and all of the quality requirements. The preliminary results indicate that the proposed interface is a powerful tool in optimizing process parameters and tightening performance specifications. Objectives that the proposed research will resolve include: (1) adaptive refinement of process behavior and feasibility; (2) dynamic tuning of process settings and quality requirements; and (3) development of an easy to use system with data handling and regression. The proposed research will have a broad impact on process and quality control by extending and rationalizing Six Sigma and other techniques currently utilized in manufacturing enterprises. With the developed tool, manufacturers can synthesize and archive process information, gain insight into their process' behavior, and rapidly converge to more optimal process configurations. Such an intuitive interface, considering the interaction of multiple process parameters and quality requirements as well as the effects of variation and uncertainty, is not available in any other decision making approach available today. Furthermore, the decision support systems derived from the proposed research can appeal to current users of spreadsheet models and other forms of simulation with applications in engineering design, manufacturing, finance, insurance, and military operations.