Analysis of the Message Queueing Telemetry Transport Protocol for Data Labelling: An Orthopedic Manufacturing Process Case Study

The recent increase in computational capability has led to an unprecedented increase in the range of new applications where machine learning can be used in real time. Notwithstanding the range of use cases where automation is now feasible, humans are likely to retain a critical role in the operation and certification of manufacturing systems for the foreseeable future. This paper presents a use case review of how human operators affect the performance of cyber–physical systems within a ’smart’ or ’cognitive’ setting. Such applications are classified using Industry 4.0 (I4.0) or 5.0 (I5.0) terminology. The authors argue that, as there is often no general agreement as to when a specific use case moves from being an I4.0 to an I5.0 example, the use of a hybrid Industry X.0 notation at the intersection between I4.0 and I5.0 is warranted. Through a structured review of the literature, the focus is on how secure human-mediated autonomous production can be performed most effectively to augment and optimise machine operation.

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Human-in-Loop: A Review of Smart Manufacturing Deployments

Bhattacharya

Penica

O’Connell

et al. 2023

Systems

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Analysis of the Message Queueing Telemetry Transport Protocol for Data Labelling: An Orthopedic Manufacturing Process Case Study

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Human-in-Loop: A Review of Smart Manufacturing Deployments

Human-in-Loop: A Review of Smart Manufacturing Deployments

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