This grant provides funding to pursue physics-based and data driven approaches for real-time prediction of performance outputs of manufacturing systems (e.g., throughputs and yield rates) based on nonlinear stochastic dynamic systems principles. Modern manufacturing enterprises have invested in a variety of sensors and IT infrastructure to increase plant floor systems visibility. This offers an unprecedented opportunity to track performance of a manufacturing system from a dynamic, as opposed to a static sense. Conventional static models are inadequate for predicting performance variables in real-time from these large data sources. Dynamic models, such as those resulting from the proposed research, are necessary. Unlike previous approaches, degradation and repair dynamics that influence the down time distributions in a manufacturing line will be explicitly considered in the proposed approach. Sigmoidal function theory will be used to remove discontinuities in the models. The commonly used stationarity assumptions will be relaxed and model order reduced, to promote fast and accurate real-time performance estimation under nonstationary regimes. The proposed approach will be validated using multiple data sets acquired from actual operations at General Motor's (GOALI partner) automotive assembly lines.
If successful, the results of the proposed research will lead to improvements in simulation modeling and real-time performance prediction of real-world manufacturing systems, including in automotive and semiconductor industry. Fast and accurate simulation models as well as performance predictors are becoming increasingly necessary to make the modern industries adapt their operations to demand fluctuations as well as supply and capacity variations. As the domestic industrial manufacturing is moving to ?assemble-to-order? paradigm, the proposed approach can be extremely valuable means to track and predict performance to support decision-making over short- and medium-term horizons.
