This Faculty Early Career Development (CAREER) research proposes to provide funding to develop, implement, and teach a multilevel, self-improving variation modeling and diagnosis methodology for complex manufacturing processes. The growing demand for products with improved functionality and time to market puts an enormous strain on production systems, resulting in ever-growing multilevel (i.e., both process level and station level) complexity in manufacturing processes. Targeting on these complexities, the research consists of several key steps. First, an efficient iterative model-building technique will be developed to identify the complex process-level variation flow. With the process-level model, the propagated variation and station-level local variation can be separated. Then, the spatial and temporal patterns of the quality data due to local variation sources will be gradually learned from the data and accumulated to form a self-improving signature library. Finally, the variation source diagnosis and process design evaluation are achieved based on this model. In addition to research, this project includes a substantial education component that includes curriculum and lab development, student advising, involvement of students from underrepresented groups, and various outreach activities including industry participation, high school involvement, and international collaboration.
If successful, the results of this research will fill the research gap in the control of complex processes by providing holistic modeling of process- and station- level complexities, effective diagnostic capability, and generic applicability to various processes, and thus provide a substantial boost to the overall competitiveness of US industries. The integrated education activities will contribute to manufacturing workforce training. Beyond manufacturing, the success of the project will also provide generic modeling and analysis tools for systems with complex flows of information. Broad dissemination of the developed methodologies could lead to diffusion to other fields vital to the nation's economic growth and security.
