The exchange of data between machines in manufacturing environments will play an essential role in near future, e.g. for negotiating production resources or for the autonomous ordering of parts and raw materials required for production.

Problem and Description

Particularly in SMEs where cyber-physical and embedded systems (CPES) are not adopted yet, machines will be gradually upgraded with embedded processing capabilities. These new breed of machines will be concurrently enhanced with “machine wallets“ manifested in CPES for conducting autonomous transactions while executing „smart contracts“ according to pre-defined business rules in order to maintain economic relationships with the participants of a manufacturing ecosystem. The objective of the proposed application experiment is to implement and operate an AI-powered Distributed Ledger Technology (DLT) Infrastructure in a CPES-reluctant SME manufacturing environment. The application experiment (AE) will be employed, conducted, and demonstrated at the facility of a rubber component (rubber bellow) manufacturer serving a supply network consisting of stakeholders from the utility vehicle and medical sectors. The innovation capacity of the experiment is significant due to the fact that DLT such as Blockchain Technology combined with AI has the potential of fostering liability of machine relationships through a tamper-proof documentation of every single transaction powered by innovative and mutually agreed smart contracts being executed on the digital ledger. In the application experiment we will for the first time make Data Stream Analytics interoperable with a DLT infrastructure, combing the best of both worlds. The AI-powered Data Stream Analytics Stack will represent a vital service of the manufacturing network by providing failure detection & diagnosis of machine data, comprehensive visualisation of machine data through dashboards, as well as an AI-based authentication of the CPES validator nodes.

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