Towards Cognitive Intelligence-Enabled Manufacturing

Agbozo, Reuben Seyram Komla; Zheng, Pai; Peng, Tao; Tang, Renzhong

doi:10.1007/978-3-031-16411-8_50

Cited by 2 publications

(2 citation statements)

References 17 publications

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“…"These cognitive processes consist of the perception of the environment, its interpretation, the crosslink with existing knowledge and the subsequent decision with a coupled action" (Sira, 2022) "Extracts applicable information together automatically and employs analytics to get an understanding of the manufacturing process. It robotizes reactions towards its findings and offers practical information being able to steadily deliver updated knowledge to decision-makers" (Seyram et al, 2022) "Using machines to utilize technologies that mimic human cognitive abilities to solve complex problems in manufacturing. "…”

Section: Referencementioning

confidence: 99%

Cognitive manufacturing: definition and current trends

El Kalach,

Yousif,

Wuest

et al. 2024

J Intell Manuf

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Manufacturing systems have recently witnessed a shift from the widely adopted automated systems seen throughout industry. The evolution of Industry 4.0 or Smart Manufacturing has led to the introduction of more autonomous systems focused on fault tolerant and customized production. These systems are required to utilize multimodal data such as machine status, sensory data, and domain knowledge for complex decision making processes. This level of intelligence can allow manufacturing systems to keep up with the ever-changing markets and intricate supply chain. Current manufacturing lines lack these capabilities and fall short of utilizing all generated data. This paper delves into the literature aiming at achieving this level of complexity. Firstly, it introduces cognitive manufacturing as a distinct research domain and proposes a definition by drawing upon various preexisting themes. Secondly, it outlines the capabilities brought forth by cognitive manufacturing, accompanied by an exploration of the associated trends and technologies. This contributes to establishing the foundation for future research in this promising field.

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Section: Referencementioning

confidence: 99%

Cognitive manufacturing: definition and current trends

El Kalach,

Yousif,

Wuest

et al. 2024

J Intell Manuf

View full text Add to dashboard Cite

show abstract

“…Hence, this paper proposes adopting and implementing cognitive intelligenceenabled systems to facilitate green manufacturing objectives. The recent advancements and publications on the conscious Turing machine [11] , cognitive computing [12] , cognitive manufacturing [13] , cognitive intelligenceenabled manufacturing systems [14] , and related fields provide the opportunity to research the technology's applicability in green manufacturing. Indeed, big data is crucial and fundamental for cognitive systems to achieve higher performance.…”

Section: Research Gaps In Big Green Data For Greentelligencementioning

confidence: 99%

Enhancing big data for greentelligence across the production value chain

Agbozo¹,

Tao²,

Cao³

et al. 2022

Green Manuf Open

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The big data concept has been explosive, revealing, and transformative across manufacturing industries as it provides deeper insights into manufacturing operations for decision-making. However, big green data (BGA), a dedicated subset of big data, is not adequately structured for comprehensive sustainability analysis, particularly in smart factories. With our proposed green data balance (GDB), there will be accountability for each input and output composition in a production unit within the production value chain (PVC). Data will be exhaustively and accurately collected in each workshop to help uncover unknown issues in a production value chain while facilitating the development of sustainability metrics or index systems. Additionally, a structured big green data system will fuel “greentelligence”, using intelligent systems and technologies to speed up digitalization toward sustainable manufacturing by measuring, tracking, and minimizing adverse environmental impacts. Lastly, with the support of the cognitive intelligence data analytic system (CIDAS), real-time and near real-time comprehensive sustainability analytics can be performed, leading to Self-X metacognitive adjustments and corrective actions.

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Towards Cognitive Intelligence-Enabled Manufacturing

Cited by 2 publications

References 17 publications

Cognitive manufacturing: definition and current trends

Cognitive manufacturing: definition and current trends

Enhancing big data for greentelligence across the production value chain

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