Deep Learning Applications in Smart Manufacturing for Revitalizing the U.S. Defense Sector
Keywords:
Smart Manufacturing, Defense SectorAbstract
Since 2009, the annual U.S. defense budget has exceeded $600B. However, the supply chain and infrastructure supporting the defense sector in the U.S. continues being challenged due to high overhead costs and an insufficient pool of manufacturers to support the Department of Defense (DoD). The defense capabilities of the nation can be classified as reliant on foreign countries (i.e., commercial and consumer electronics) or domestic (i.e., microelectronics and capital systems). The U.S. is becoming increasingly reliant and vulnerable to foreign nations with respect to critical technologies and supply chains. This issue is further exacerbating with the COVID-19 pandemic.
This research analyzes how the adoption and advancement of technologies traditionally considered for commercial applications (Internet-of-Things, Artificial Intelligence, and Digital Twin Technology) are being applied and customized for the factories that support the nation’s defense capabilities. A voluntarily-participating consortium of manufacturers in the U.S. defense industry is being established in cooperation with major Integrated Defense Primes and the DoD. This consortium will focus on the development and adoption of technology enablers (sensors, data aggregation platforms, analytics engines, predictive engines) and solutions (supply chain risk assessments, equipment over/under utilization assessments, multi-plant MRP optimization) that support the Foundational and Advanced Applications of Smart Manufacturing. These technologies and solutions will enable members of the consortium to improve their factory performance and efficiency over a greater time horizon and across multiple sites [1].
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