The Intelligent Factory Space – A Concept for Observing, Learning and Communicating in the Digitalized Factory

Reimann, Jan; Sziebig, Gábor

doi:10.1109/access.2019.2919340

Cited by 20 publications

(11 citation statements)

References 33 publications

(25 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…To further improve the accuracy and the quality of industrial processes, the Intelligent Factory Space (IFS), an AI-inspired architecture that facilitates the interaction between humans and robots, was presented in Reference [93], emphasising on the safety and trust aspects, during the two-way communication between humans and robots. Also, the notion of patented intelligence (Pi)-Mind was described in detail in Reference [94], as an Intelligence-as-a-Service (IaaS) provider that transparently and proactively introduces human expertise on decision-making based on self-awareness.…”

Section: Theoretical Frameworkmentioning

confidence: 99%

Tackling Faults in the Industry 4.0 Era—A Survey of Machine-Learning Solutions and Key Aspects

Angelopoulos

Michailidis

Νομικός

et al. 2019

Sensors

220

View full text Add to dashboard Cite

The recent advancements in the fields of artificial intelligence (AI) and machine learning (ML) have affected several research fields, leading to improvements that could not have been possible with conventional optimization techniques. Among the sectors where AI/ML enables a plethora of opportunities, industrial manufacturing can expect significant gains from the increased process automation. At the same time, the introduction of the Industrial Internet of Things (IIoT), providing improved wireless connectivity for real-time manufacturing data collection and processing, has resulted in the culmination of the fourth industrial revolution, also known as Industry 4.0. In this survey, we focus on the vital processes of fault detection, prediction and prevention in Industry 4.0 and present recent developments in ML-based solutions. We start by examining various proposed cloud/fog/edge architectures, highlighting their importance for acquiring manufacturing data in order to train the ML algorithms. In addition, as faults might also occur from sources beyond machine degradation, the potential of ML in safeguarding cyber-security is thoroughly discussed. Moreover, a major concern in the Industry 4.0 ecosystem is the role of human operators and workers. Towards this end, a detailed overview of ML-based human–machine interaction techniques is provided, allowing humans to be in-the-loop of the manufacturing processes in a symbiotic manner with minimal errors. Finally, open issues in these relevant fields are given, stimulating further research.

show abstract

Section: Theoretical Frameworkmentioning

confidence: 99%

Tackling Faults in the Industry 4.0 Era—A Survey of Machine-Learning Solutions and Key Aspects

Angelopoulos

Michailidis

Νομικός

et al. 2019

Sensors

220

View full text Add to dashboard Cite

show abstract

“…5. Automation and robotization of administrative business processes (planning, analysis, control) using RPA, digitallabor, intelligentautomation, processmining technologies will significantly reduce the number of personnel in economic and financial services at metallurgical enterprises [14].…”

Section: Resultsmentioning

confidence: 99%

Formation of the capital structure of metallurgical industry companies in conditions of sustainable industry development 4.0

Panfilova

Lvovа

2020

E3S Web Conf.

View full text Add to dashboard Cite

The article deals with current issues of digital transformation of metallurgical companies in the conditions of sustainable development of industry 4.0. The authors systematize the tools of through digitalization of companies in the metallurgical industry, give their characteristics, as well as proposed a mechanism for the impact of digital tools on the capital structure and financial stability. At the same time the analysis of the capital of the leading metallurgical company of Russia and the world JSC “Uralelectromed” confirms the necessity to consider the formation of the capital structure by digital methods of management in the aspect of sus-tainable development.

show abstract

“…4 Illustrative sets of actions, functions, requirements, characteristics, and behaviors for human operators and robots which are candidates for application of AI/ML approaches Ref. [90] presents a layered architecture for HRC connected to higher-level global plant functions and supporting the use of AI tools. This framework allows for data collected at the level of an HRC work cell to train for unknown/unplanned scenarios that can be detected during operation via supervised learning algorithms that detect known categories of failure scenarios.…”

Section: Intervenementioning

confidence: 99%

Artificial Intelligence in Advanced Manufacturing: Current Status and Future Outlook

Arinez

Chang

Gao

et al. 2020

Journal of Manufacturing Science and Engineering

240

View full text Add to dashboard Cite

Today's manufacturing systems are becoming increasingly complex, dynamic and connected. The factory operation faces challenges of highly nonlinear and stochastic activity due to the countless uncertainties and interdependencies that exist. Recent developments in Artificial Intelligence (AI), especially Machine Learning (ML) have shown great potential to transform the manufacturing domain through advanced analytics tools for processing the vast amounts of manufacturing data generated, known as Big Data. The focus of this paper is threefold: (1) Review the State-of-the-Art applications of AI to representative manufacturing problems, (2) Provide a systematic view for analyzing data and process dependencies at multiple levels that AI must comprehend, and (3) Identify challenges and opportunities to not only further leverage AI for manufacturing, but also influence the future development of AI to better meet the needs of manufacturing. To satisfy these objectives, the paper adopts the hierarchical organization widely practiced in manufacturing plants in examining the interdependencies from the overall system level to the more detailed granular level of incoming material process streams. In doing so, the paper considers a wide range of topics from throughput and quality, supervisory control in human robotic collaboration, process monitoring, diagnosis and prognosis, finally to advances in materials engineering to achieve desired material property in process modeling and control.

show abstract

The Intelligent Factory Space – A Concept for Observing, Learning and Communicating in the Digitalized Factory

Cited by 20 publications

References 33 publications

Tackling Faults in the Industry 4.0 Era—A Survey of Machine-Learning Solutions and Key Aspects

Tackling Faults in the Industry 4.0 Era—A Survey of Machine-Learning Solutions and Key Aspects

Formation of the capital structure of metallurgical industry companies in conditions of sustainable industry development 4.0

Artificial Intelligence in Advanced Manufacturing: Current Status and Future Outlook

Contact Info

Product

Resources

About