Abstract
Manufacturing is imperative to economic growth, prosperity, and sustainment, and a higher standard of living. Impacted by technological, economic, environmental, social, public health, and governmental fluxes; global and competitive markets have created new challenges for the manufacturing industry. Coherent and autonomous decision-making using massive datasets is among one of the most critical new capabilities needed to ensure sustainability of today’s manufacturing technology as well as other enterprise and critical infrastructures. Such an autonomous and coherent decision-making capability relies heavily on advanced modeling which incorporates timely analysis through system monitoring and assessment based on the most recent data. Concentrating on the modeling problem along with the technological advances in distributed sensor networks, monitoring, communications, and computational resources, the Dynamic Data Driven Applications Systems (DDDAS) paradigm presents an opportunity to bridge the gap between such autonomous control and data processing. The novelty of the DDDAS comes from its ability to adjust the fidelity of a system’s application model, by incorporating dynamic data into the executing model, which then steers the measurement process for selective data update used by the model either to improve its accuracy or to speed the model; such approaches, combined with adaptivity to available computational resources, lead to decision support systems with the accuracy of full-scale models. This chapter presents the applicability of the DDDAS paradigm to multiple advanced manufacturing areas, namely, nanomanufacturing, materials by design, biomanufacturing, advanced robotics, and e-manufacturing whose success is vital to the future of manufacturing, innovation, and thereof its technology management. This chapter describes each of these manufacturing areas, their major characteristics, and potential opportunities that are expected to benefit from the DDDAS. To illustrate an efficacious implementation of DDDAS to fulfill the needs of advanced manufacturing, its application for a semiconductor die manufacturing facility is demonstrated as a recent case study. Using the DDDAS approach, significant improvement is reported in the meantime cycle of the considered semiconductor manufacturing die facility re-entrant flow process.