GOALI: Causation-Based Quality Control - A New Paradigm to Achieve Effective Monitoring, Diagnosis, and Control for Complex Manufacturing Systems

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This grant funds the development of "causation-based" quality control methodologies. A causal model, represented by a general probabilistic network or a probabilistic graphical model, is adopted to represent causal relationships among quality and process variables. The first effort is to develop engineering knowledge enhanced causal modeling with the focus on integrating the engineering domain knowledge into the causal structure learning and parameter estimation with the consideration of sensing data uncertainties. Based on the causal model, causation-based monitoring and diagnostic algorithms will be developed through explicit decomposition according to their causal relationships for diagnostic capability enhancement. In addition, on-line causation-based control and intervention will be studied by integrating cautious control principles with the causal model considering the uncertainties of both model parameters and intervention. Finally, the developed methodologies will be validated and implemented in multistage forming processes through a GOALI effort with industrial companies. Industrial companies will provide real data, cases, and manufacturing process information for the research team. International collaborative research and education efforts will be pursued throughout the research project.The success of this project will advance the state of the art in modeling and control of complex systems by contributing new concepts, criteria, and algorithms to the information-processing capabilities for quality improvement. The project creates enabling methodologies to bring a transition from a traditional SPC to a proactive, real-time diagnostic and predictive control paradigm in a data-rich environment. The developed causation-based quality control methodologies will provide algorithms and associated software tools to achieve (i) causation (rather than only correlation) based process modeling and analysis; (ii) embedded diagnostic monitoring and root cause identification (rather than only change detection); and (iii) on-line quality inference and intervention for defect prevention (rather than off-line simulation and quality inspection). The developed methodology addresses critical issues in a data rich environment, which is a problem challenging all industry sectors. The implementation of the resulting methodologies is expected to generate broad economic impacts.