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 resistance to disturbances. Such production lines would be able to reallocate resources as needed and minimize downtime while keeping up with market demands. These systems must be capable of complex decision-making based on parameters such as machine status, sensory/IoT 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. This article presents progress towards this autonomy by introducing Semantic Web capabilities applied to managing the production line. 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 diverse data sources and domain knowledge to complete a manufacturing process despite malfunctioning equipment. It highlights the benefit of Semantic Web in manufacturing by integrating the heterogeneous information required for the process to be completed. This provides an approach to autonomous manufacturing not yet fully realized at the intersection of Semantic Web and manufacturing.
Digital Object Identifier (DOI)
Postprint version. Published in IEEE Intelligent Systems, Volume 38, Issue 1, 2023, pages 69-75.
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Kalach, F. E., Wickramarachchi, R., Harik, R., & Sheth, A. (2023). A semantic web approach to fault tolerant autonomous manufacturing. IEEE Intelligent Systems, 38(1), 69–75. https://doi.org/10.1109/MIS.2023.3235677
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