Enabling smart manufacturing through a systematic planning framework for edge computing

Zietsch, Jakob; Vogt, Marcus; Lee, Benjamin D.; Herrmann, Christoph; Thiede, Sebastian

doi:10.1016/j.cirpj.2020.06.010

Cited by 19 publications

(2 citation statements)

References 34 publications

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“…Indeed, EC and FC would allow to tackle some challenges of CC, such as the low efficiency in analyzing a large amount of data in short time and the negative impact of the Quality of Service (QoS). However, although EC and FC enhance the energy saving consumption and resources utilization, there still exist some open issues in their implementation (Zietsch et al, 2020;Laroui et al, 2021).…”

Section: Jmtm 344mentioning

confidence: 99%

An IoT-based and cloud-assisted AI-driven monitoring platform for smart manufacturing: design architecture and experimental validation

Petrillo

Caiazzo

Piccirillo

et al. 2022

JMTM

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PurposeThis work aims at proposing a novel Internet of Things (IoT)-based and cloud-assisted monitoring architecture for smart manufacturing systems able to evaluate their overall status and detect eventual anomalies occurring into the production. A novel artificial intelligence (AI) based technique, able to identify the specific anomalous event and the related risk classification for possible intervention, is hence proposed.Design/methodology/approachThe proposed solution is a five-layer scalable and modular platform in Industry 5.0 perspective, where the crucial layer is the Cloud Cyber one. This embeds a novel anomaly detection solution, designed by leveraging control charts, autoencoders (AE) long short-term memory (LSTM) and Fuzzy Inference System (FIS). The proper combination of these methods allows, not only detecting the products defects, but also recognizing their causalities.FindingsThe proposed architecture, experimentally validated on a manufacturing system involved into the production of a solar thermal high-vacuum flat panel, provides to human operators information about anomalous events, where they occur, and crucial information about their risk levels.Practical implicationsThanks to the abnormal risk panel; human operators and business managers are able, not only of remotely visualizing the real-time status of each production parameter, but also to properly face with the eventual anomalous events, only when necessary. This is especially relevant in an emergency situation, such as the COVID-19 pandemic.Originality/valueThe monitoring platform is one of the first attempts in leading modern manufacturing systems toward the Industry 5.0 concept. Indeed, it combines human strengths, IoT technology on machines, cloud-based solutions with AI and zero detect manufacturing strategies in a unified framework so to detect causalities in complex dynamic systems by enabling the possibility of products’ waste avoidance.

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Section: Jmtm 344mentioning

confidence: 99%

An IoT-based and cloud-assisted AI-driven monitoring platform for smart manufacturing: design architecture and experimental validation

Petrillo

Caiazzo

Piccirillo

et al. 2022

JMTM

View full text Add to dashboard Cite

show abstract

“…The data «aging» makes it seriously indefinite for a CPP control, which has the automatics primary elements of homogeneous nature for industrial systems and telecommunication connection systems. Different CPS devices single network with a feedback engages the information exchange wireless channel in a control loop, which regulate measuring and computing operations of [9,10]:…”

Section: Introductionmentioning

confidence: 99%

Edge, fog and cloud computing in the cyber-physical systems networks

2021

Proceedings of the III International Workshop on Modeling, Information Processing and Computing (MIP: Computing-2021)

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The data processing and technological tasks completion parallelism are the distributed control system base which use in a cyber-physical production edge, fog and cloud computing. The mutual agreement of operation and informative production processes is provided with a network message exchange technology supported with a functionally independent cyberphysical systems. To control a message queue and industrial equipment actions coordination they use communication system elements with data tide processing function in edge, fog and cloud network units and client applications placed in cyber-physical system devices. The industrial equipment own control processes are regulated with a local platform with edge computing. The expanded cyber-physical production technological task is controlled with a multi-process computing platform. To increase the quality of message in fact received with a cyber-physical system and to make faster its data processing is done with fog computing engaging equipment functional groups resources divided in a general information process. To transmit the technological task solution components into the network edge and to implement the fog computing of high parallelism degree in the production infrastructure is a perspective Industry 4.0 approach to organize the production systems interaction. They propose a cyberphysical production structure based on network technologies, which use edge, fog and cloud computing.

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Consistent Contextualisation of Process and Quality Information for Machining Processes

Fertig¹,

Kohn²,

Brockhaus³

et al. 2021

Lecture Notes in Production Engineering

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Enabling smart manufacturing through a systematic planning framework for edge computing

Cited by 19 publications

References 34 publications

An IoT-based and cloud-assisted AI-driven monitoring platform for smart manufacturing: design architecture and experimental validation

An IoT-based and cloud-assisted AI-driven monitoring platform for smart manufacturing: design architecture and experimental validation

Edge, fog and cloud computing in the cyber-physical systems networks

Consistent Contextualisation of Process and Quality Information for Machining Processes

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