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 process monitoring is investigated. A physics based compressive sensing (PBCS) approach is proposed to reduce the number of sensors and amount of data collection associated with additive manufacturing process monitoring. PBCS can significantly improve the compression ratio from traditional compressed sensing by incorporating the knowledge of physical phenomena in specific applications. PBCS has been demonstrated to monitor the temperature distribution in fused filament fabrication process and the velocity field in the backward-facing step flow. PBCS will be extended to monitor the multi-physics system such as the direct energy deposition process. The sensing performance will be improved by optimizing the sensor placement with dictionary learning approaches, so that values on boundaries which are required for PBCS can be predicted with a few measurements. The proposed PBCS scheme provides a systematic and rigorous approach to design efficient sensing protocols for future manufacturing systems, where sensors are ubiquitously utilized in monitoring process and quality.
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MATIN Development Team