SUMMARY

Sensors play an important role in smart manufacturing. Different types of sensors have been used in process monitoring to ensure the quality of products. As a result, the life-cycle cost of quality control is rising. The reliability of sensors also affects the reliability of complex systems with a large number of sensors onboard. Another challenge is the available bandwidth in communication channels for transmission of large volumes of data. The original purpose of data cannot be fulfilled if they are not shared and used. In this research, a new approach that uses low-fidelity measurements with limited sensors to provide high-fidelity information in additive manufacturing (AM) process monitoring is investigated. A physics based compressive sensing (PBCS) framework is proposed to reduce the number of sensors and amount of data collection, which significantly improves the compression ratio from traditional compressed sensing by incorporating the knowledge of physical phenomena in specific applications. By solving the inverse problems, the PBCS framework will be used to reconstruct three-dimensional temperature and fluid velocity fields in AM processes based on limited measurements. The sensing performance will also be improved by optimizing the sensor locations via dictionary learning. The systematic error of PBCS can be predicted and compensated based on a Gaussian process approach. The proposed PBCS scheme provides a systematic and rigorous approach to design efficient sensing protocols for future manufacturing systems.