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Blockchain is a new generation of secure information technology that is fueling business and industrial innovation. Many studies on key enabling technologies for resource organization and system operation of blockchain-secured smart manufacturing in Industry 4.0 had been conducted. However, the progression and promotion of these blockchain applications have been fundamentally impeded by various issues in scalability, flexibility, and cybersecurity. This survey discusses how blockchain systems can overcome potential cybersecurity barriers to achieving intelligence in Industry 4.0. In this regard, eight cybersecurity issues (CIs) are identified in manufacturing systems. Ten metrics for implementing blockchain applications in the manufacturing system are devised while surveying research in blockchain-secured smart manufacturing. This study reveals how these CIs have been studied in the literature. Based on insights obtained from this analysis, future research directions for blockchain-secured smart manufacturing are presented, which potentially guides research on urgent cybersecurity concerns for achieving intelligence in Industry 4.0. [ABSTRACT FROM AUTHOR]

The growth of individualized product demands drives high flexibility of manufacturing processes, which requires large-scale deployment of Industrial Internet of Things (IIoT). Since centralized control of IIoT suffers from poor flexibility in coping with disturbances and changes, a decentralized organization structure is a better choice, in which a permissioned blockchain-driven IIoT can enable partially decentralized self-organization and thus offload and accelerate the optimization of upper-level manufacturing planning. A novel iterative bi-level hybrid intelligence model named ManuChain is proposed to get rid of unbalance/inconsistency between holistic planning and local execution in individualized manufacturing systems. Lower-level blockchain-driven smart contracts proactively decentralize fine-grained and individualized task execution among machine tools via Raspberry Pi-based smart gateways and make the results available on an upper-level digital twin model for iterative coarse-grained holistic optimization. A prototype ManuChain based on a permissioned blockchain network is presented to realize both lower-level crowd self-organizing intelligence and upper-level holistic optimization intelligence. [ABSTRACT FROM AUTHOR]

There is an increasing use of social interaction contexts in the cross-enterprise manufacturing problem solving. To transform these massive and unstructured data into decision-support information for cross-enterprise manufacturing demand-capability matching, we present automated solutions to two phases: 1) extracting relationships based on a semi-supervised learning approach to derive formalized heterogeneous manufacturing network from the unstructured text-based context that contains high levels of noise and irrelevant information and 2) matching group-level relationships among the entities in the established manufacturing network. The extracting phase formulates network data using multiattributed graph that can encode various entities and relationships. The matching phase is based on probabilistic multiattributed graph matching, and implemented using distributed message passing algorithm. We developed a prototype system to verify the proposed model, which is also flexible to new domains of contexts and scale to large datasets. The ultimate goal of this paper is to facilitate knowledge transferring and sharing in the context of cross-enterprise social interaction, thereby supporting the integration of the resources and capabilities among different enterprise. [ABSTRACT FROM PUBLISHER]