Industry 4.0 Science 39, 2023, 95-99
Integration of Artificial Intelligence into Factory Control

Bibtex

Cite as text

						@Article{Gronau,
							Cite-key = "Gronau2023Int", 
							Year= "2023", 
							Number= "1", 
							 Volume= "Industry 4.0 Science 39", 
							Pages= "95-99", 
							Journal   = "Industry 4.0 Science",
							 Title= "Integration of Artificial Intelligence into Factory Control", 
							Author= "Norbert Gronau, University of Potsdam", 
							Doi= "https://doi.org/10.30844/I4SE.23.1.95", 
							 Abstract= "With the increasing availability of IoT devices and significantly greater incorporation of Internet-enabled technologies into manufacturing processes, the idea of improving factory control through the use of artificial
intelligence (AI) is also coming to the fore. Using the example of highvariation series manufacturing, this article describes which steps need to
be taken to improve factory control with AI.", 
							 Keywords= "AI, Artificial Intelligence, factory control, smart factory, Deep Learning, Machine Learning, high-variation series manufacturing, variants, variant sequence
planning, AI in factories, AI-integrated manufacturing, throughput optimization", 
							}
					
Norbert Gronau, University of Potsdam(2023): Integration of Artificial Intelligence into Factory Control. Industry 4.0 Science 391(2023), S. 95-99. Online: https://doi.org/10.30844/I4SE.23.1.95 (Abgerufen 17.04.24)

Abstract

Abstract

With the increasing availability of IoT devices and significantly greater incorporation of Internet-enabled technologies into manufacturing processes, the idea of improving factory control through the use of artificial intelligence (AI) is also coming to the fore. Using the example of highvariation series manufacturing, this article describes which steps need to be taken to improve factory control with AI.

Keywords

Schlüsselwörter

AI, Artificial Intelligence, factory control, smart factory, Deep Learning, Machine Learning, high-variation series manufacturing, variants, variant sequence planning, AI in factories, AI-integrated manufacturing, throughput optimization

References

Referenzen

[1] URL: www.guru99. com/images/tensorflow/083018_0454_MachineLear5.png, accessed Oct 11, 2019.
[2] Weber, E., Tiefenbacher, A., Gronau, N.: Need for Standardization and Systematization of Test Data for Job-Shop Scheduling. In: Data 2019, 4 (1), 32; DOI: doi.org.10.3390/ data4010032, accessed Oct 11, 2019.
[3] Lass, S.: Nutzenvalidierung cyber-physischer Systeme in komplexen Fabrikumgebungen. Berlin 2017.

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