Date of Award
Open Access Thesis
The next phase of manufacturing is centered on making the switch from traditional automated to autonomous systems. Future Factories are required to be agile, allowing for more customized production, and resistant to disturbances. Such production lines would have the capability to reallocate resources as needed and eliminate downtime while keeping up with market demands. These systems must be capable of complex decision making based on different parameters such as machine status, sensory data, and inspection results. Current manufacturing lines lack this complex capability and instead focus on low level decision making on the machine level without utilizing the generated data to its full extent. Thisthesis presents progresstowards autonomy by developing a data exchange architecture and introducing Semantic Web capabilities applied to managing the production line. The architecture consists of three layers. The Equipment Layer includes the industrial assets of the factory, the Shop Floor Layer supports edge analytic capabilities converting raw sensory data to actionable information, and the Enterprise Layer acts as the hub of all information. Finally, a full autonomous manufacturing use case is also developed to showcase the value of Semantic Web in a manufacturing context. This use case utilizes different data sources to complete a manufacturing process despite malfunctioning equipment. This provides an approach to autonomous manufacturing not yet fully realized at the intersection of three paradigms: Smart Manufacturing, Autonomous Manufacturing, and Semantic Web.
Kalach, F. E.(2023). A Semantic Web Approach to Fault Tolerant Autonomous Manufacturing. (Master's thesis). Retrieved from https://scholarcommons.sc.edu/etd/7351