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In the practice of cloud manufacturing, there still exist some major challenges, including: 1) cloud based big data analytics and decision-making cannot meet the requirements of many latencysensitive applications on shop floors; 2) existing manufacturing systems lack enough reconfigurability, openness and evolvability to deal with shop-floor disturbances and market changes; and 3) big data from shop-floors and the Internet has not been effectively utilized to guide the optimization and upgrade<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1109/access.2020.2977846">doi:10.1109/access.2020.2977846</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/zsrf52qhyvd7zi6zghstoiht7a">fatcat:zsrf52qhyvd7zi6zghstoiht7a</a> </span>
more »... f manufacturing systems. This paper proposes an open evolutionary architecture of the intelligent cloud manufacturing system with collaborative edge and cloud processing. Hierarchical gateways connecting and managing shop-floor things at the "edge" side are introduced to support latency-sensitive applications for real-time responses. Big data processed both at the gateways and in the cloud will be used to guide continuous improvement and evolution of edge-cloud systems for better performance. As software tools are becoming dominant as the "brain" of manufacturing control and decision-making, this paper also proposes a new mode -"AI-Mfg-Ops" (AI enabled Manufacturing Operations) with a supporting software defined framework, which can promote fast operation and upgrading of cloud manufacturing systems with smart monitoring-analysis-planning-execution in a closed loop. This research can contribute to the rapid response and efficient operation of cloud manufacturing systems. INDEX TERMS Cloud manufacturing, big data, edge-cloud collaboration, software-defined architecture, Internet of Things.
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