SAMBA – an architecture for adaptive cognitive control of distributed Cyber-Physical Production Systems based on its self-awareness

Siafara, Lydia C.; Kholerdi, Hedyeh A.; Братухин, А. Г.; TaheriNejad, Nima; Jantsch, Axel

doi:10.1007/s00502-018-0614-7

Cited by 21 publications

(18 citation statements)

References 18 publications

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“…Manufacturing integrates and is altered by cognitive technologies (Sharma et al, 2021) by use of product decisionmaking information systems, cyber-physical system-based real-time moni-toring, robotic wireless sensor networks, and Internet of Things smart devices (Bailey, 2021;Hudson, 2022;Pelau et al, 2021;Vătămănescu et al, 2022) in sustainable product lifecycle management. The integration of artificial intelligence data-driven Internet of Things systems and real-time advanced analytics (Krüger et al, 2016;Li et al, 2015;Palombarini & Martínez, 2012;Siafara et al, 2018) has furthered the swift advancement of Internet of Things-based real-time production logistics.…”

Section: Discussionmentioning

confidence: 99%

“…In this research, prior findings have been cumulated (Li et al, 2015;Palombarini & Martínez, 2012;Siafara et al, 2018;Din et al, 2019) clarifying that artificial intelligence data-driven Internet of Things systems (Cavallo et al, 2021;Chung & Yoo, 2020;Li et al, 2021b;Qin & Lu, 2021), deep learning-assisted smart process planning (Elia & Margherita, 2021;Gain, 2021;Liu et al, 2022;Penumuru et al, 2020), and real-time sensor networks (Ferreras-Higuero et al, 2020;Ksentini et al, 2021;Hu et al, 2016;Ding et al, 2021) advance constantly optimized smart manufacturing systems. In this research, previous published findings have been cumulated clarifying that cognitive capabilities lead to increased flexibility and variability (Dumitrache et al, 2019;Emmer et al, 2018;Casadei et al, 2019;Hu et al, 2019) that enable streamlined production.…”

Section: Introductionmentioning

confidence: 87%

“…Cognitive manufacturing systems can provide increased value and precision in complex operational processes (Krüger et al, 2016;Li et al, 2015;Palombarini & Martínez, 2012;Siafara et al, 2018), attaining elevated quality and efficiency at decreased expenses and diminished production time. Industrial robots harnessed during machining applications can optimize a complementary, flexible, and affordable manufacturing technology in comparison with standard machine tools.…”

Section: Cutting-edge Cognitive Computing Big Data Analytics Techniqu...mentioning

confidence: 99%

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Artificial intelligence-based decision-making algorithms, Internet of Things sensing networks, and sustainable cyber-physical management systems in big data-driven cognitive manufacturing

Lăzăroiu

Androniceanu

Grecu

et al. 2022

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Research background: With increasing evidence of cognitive technologies progressively integrating themselves at all levels of the manufacturing enterprises, there is an instrumental need for comprehending how cognitive manufacturing systems can provide increased value and precision in complex operational processes. Purpose of the article: In this research, prior findings were cumulated proving that cognitive manufacturing integrates artificial intelligence-based decision-making algorithms, real-time big data analytics, sustainable industrial value creation, and digitized mass production. Methods: Throughout April and June 2022, by employing Preferred Reporting Items for Systematic Reviews and Meta-analysis (PRISMA) guidelines, a quantitative literature review of ProQuest, Scopus, and the Web of Science databases was performed, with search terms including ?cognitive Industrial Internet of Things?, ?cognitive automation?, ?cognitive manufacturing systems?, ?cognitively-enhanced machine?, ?cognitive technology-driven automation?, ?cognitive computing technologies,? and ?cognitive technologies.? The Systematic Review Data Repository (SRDR) was leveraged, a software program for the collecting, processing, and analysis of data for our research. The quality of the selected scholarly sources was evaluated by harnessing the Mixed Method Appraisal Tool (MMAT). AMSTAR (Assessing the Methodological Quality of Systematic Reviews) deployed artificial intelligence and intelligent workflows, and Dedoose was used for mixed methods research. VOSviewer layout algorithms and Dimensions bibliometric mapping served as data visualization tools. Findings & value added: Cognitive manufacturing systems is developed on sustainable product lifecycle management, Internet of Things-based real-time production logistics, and deep learning-assisted smart process planning, optimizing value creation capabilities and artificial intelligence-based decision-making algorithms. Subsequent interest should be oriented to how predictive maintenance can assist in cognitive manufacturing by use of artificial intelligence-based decision-making algorithms, real-time big data analytics, sustainable industrial value creation, and digitized mass production.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 87%

Section: Cutting-edge Cognitive Computing Big Data Analytics Techniqu...mentioning

confidence: 99%

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Artificial intelligence-based decision-making algorithms, Internet of Things sensing networks, and sustainable cyber-physical management systems in big data-driven cognitive manufacturing

Lăzăroiu

Androniceanu

Grecu

et al. 2022

View full text Add to dashboard Cite

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“…Short-term fatigue measurement [16,[19][20][21][22][23][24][25][26][27][28][29][30][31][32][33] Reduction of fatigue [7,15,24,[34][35][36][37][38][39][40][41][42][43] Integration of human short-term fatigue in an industrial decision system [2,[44][45][46][47][48][49][50][51][52][53][54][55]…”

Section: Short-term Fatiguementioning

confidence: 99%

Health-Related Parameters for Evaluation Methodologies of Human Operators in Industry: A Systematic Literature Review

et al. 2021

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Human factors have always been an important part of research in industry, but more recently the idea of sustainable development has attracted considerable interest for manufacturing companies and management practitioners. Incorporating human factors into a decision system is a difficult challenge for manufacturing companies because the data related to human factors are difficult to sense and integrate into the decision-making processes. Our objectives with this review are to propose an overview of the different methods to measure human factors, of the solutions to reduce the occupational strain for workers and of the technical solutions to integrate these measures and solutions into a complex industrial decision system. The Scopus database was systematically searched for works from 2014 to 2021 that describe some aspects of human factors in industry. We categorized these works into three different classes, representing the specificity of the studied human factor. This review aims to show the main differences between the approaches of short-term fatigue, long-term physical strain and psychosocial risks. Long-term physical strain is the subject that concentrates the most research efforts, mainly with physical and simulation techniques to highlight physical constraints at work. Short-term fatigue and psychosocial constraints have become a growing concern in industry due to new technologies that increase the requirements of cognitive activities of workers. Human factors are taking an important place in the sustainable development of industry, in order to ameliorate working conditions. However, vigilance is required because health-related data creation and exploitation are sensible for the integrity and privacy of workers.

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“…CSA has been applied to both software [44] and hardware [52]. Following applications have benefited from CSA concepts (some of them under other terms such as adaptivity, autonomy, and goal-oriented systems): mobile applications [53], object tracking with smart cameras [24], [54], artificial intelligence [55], cloud computing [56], networks [57], operating systems [58], web [59], adaptive and dynamic compilation environment [60], Multi-Processor System-on-Chip (MPSoC) resource management [61], [62], (cyber-physical) SoC [52], mobile robots [63], industrial systems [64], [65], health monitoring [22] as well as single and multi-user active music environments [66].…”

Section: Self-adaptivenessmentioning

confidence: 99%

RoSA: A Framework for Modeling Self-Awareness in Cyber-Physical Systems

et al. 2020

Self Cite

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The role of smart and autonomous systems is becoming vital in many areas of industry and society. Expectations from such systems continuously rise and become more ambitious: long lifetime, high reliability, high performance, energy efficiency, and adaptability, particularly in the presence of changing environments. Computational self-awareness promises a comprehensive assessment of the system state for sensible and well-informed actions and resource management. Computational self-awareness concepts can be used in many applications such as automated manufacturing plants, telecommunication systems, autonomous driving, traffic control, smart grids, and wearable health monitoring systems. Developing self-aware systems from scratch for each application is the most common practice currently, but this is highly redundant, inefficient, and uneconomic. Hence, we propose a framework that supports modeling and evaluation of various self-aware concepts in hierarchical agent systems, where agents are made up of self-aware functionalities. This paper presents the Research on Self-Awareness (RoSA) framework and its design principles. In addition, self-aware functionalities abstraction, data reliability, and confidence, which are currently provided by RoSA, are described. Potential use cases of RoSA are discussed. Capabilities of the proposed framework are showcased by case studies from the fields of healthcare and industrial monitoring. We believe that RoSA is capable of serving as a common framework for self-aware modeling and applications and thus helps researchers and engineers in exploring the vast design space of hierarchical agent-based systems with computational self-awareness.

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SAMBA – an architecture for adaptive cognitive control of distributed Cyber-Physical Production Systems based on its self-awareness

Cited by 21 publications

References 18 publications

Artificial intelligence-based decision-making algorithms, Internet of Things sensing networks, and sustainable cyber-physical management systems in big data-driven cognitive manufacturing

Artificial intelligence-based decision-making algorithms, Internet of Things sensing networks, and sustainable cyber-physical management systems in big data-driven cognitive manufacturing

Health-Related Parameters for Evaluation Methodologies of Human Operators in Industry: A Systematic Literature Review

RoSA: A Framework for Modeling Self-Awareness in Cyber-Physical Systems

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