The timely and accurate in-service identification of faults in mechanical structures, such as airplanes, can play a vitally important role in avoiding catastrophes. One major challenge, however, is that the sensing system relies on high frequency signals, the coordination of which is difficult to achieve throughout a large structure. To tackle this fundamental issue, the research team will take advantage of 3D printing technology to fabricate integrated sensor-structure components. Specifically, the team plans to innovate a novel printing scheme that can embed piezoelectric transducers (namely, sensor/actuator coupled elements) into layered composites. As the transducers are densely distributed throughout the entire structure, they function like a nerve system embedded into the structure. Such a sensor nerve system, when combined with new control and command systems and advanced data and signal processing capability, can fully unleash the latest computing power to pinpoint the fault location. The new framework of utilizing emerging additive manufacturing technology to produce a structural system with integrated, densely distributed active sensing elements will potentially lead to paradigm-shifting progress in structural self-diagnosis. This advancement may allow the acquisition of high-quality, active interrogation data throughout the entire structure, which can then be used to facilitate highly accurate and robust decision-making. It will lead to intellectual contributions including: 1) development of a new sensing modality with mechanical-electrical dual-field adaptivity, that yields rich and high-quality data throughout the structure; 2) design of an additive manufacturing scheme that inserts piezoelectric micro transducer arrays throughout the structure to enable active interrogation; and 3) formulation of new data analytics and inverse analysis that can accurately identify the fault location/severity and guide the fine-tuning of the sensor system.

Production as a service (PaaS) defines a new paradigm in manufacturing that will allow designers of new products to query existing manufacturing facilities and receive information about fabrication capabilities and production availability. The access to information such as part cost, part quality, and production time will help new products to be prototyped and scaled-up quickly, while also allowing existing manufacturing facilities to benefit from underutilized equipment and labor. The PaaS framework will include both a front-end query interface for the users and a back-end analysis component. The interface will be designed to connect users with small-, mid-, and large-sized manufacturing facilities, while the scheduling and routing algorithms will provide the flexibility and security protocols needed to guarantee operational and production safety across the range of facilities. Manufacturers that utilize the PaaS framework will reap the potential of meeting customer needs in terms of cost, quality, on-time delivery, while being reactive to changing market forces. With 12 percent of the GDP represented by the manufacturing industry, the manufacturing operational improvements that will result from this EArly-concept Grant for Exploratory Research (EAGER) project have the potential to make a significant impact in the national bottom line. The aim of the PaaS platform is to enable distributed manufacturing plant locations to efficiently coordinate both within one plant location as well as across plant locations to realize a flexible service interface for supporting production management. The intellectual merit of this research lies in the extensions that will be created to the existing science and technology in service-oriented architectures to enable distributed production, while preserving proprietary information of the manufacturing systems. The key software abstraction that enables this innovation comes from the extension to the well-known APIs to capture the sophisticated query logic and diverse production requirements to meet user needs. Routing and scheduling decisions will be optimized by leveraging a global view of the current state of all of the components in the manufacturing facilities. To demonstrate scalability and ensure privacy guarantees across multiple facilities, hierarchical abstraction will be used to hide low-level details and proprietary information. The PaaS framework will transform the way manufacturing companies interact with the emerging high-value market; providing the architecture to drive innovation and enable small-, mid-, and large-scale manufacturing companies across the U.S. to compete for new product business on an even playing field.