Establishing a Machine Learning and Internet of Things Learning Infrastructure by Operating Transnational Cyber-Physical Brewing Labs

Deuse, J; Wöstmann, R; Syberg, M; West, N; Wagstyl, D; Moreno, VH

Establishing a Machine Learning and Internet of Things Learning Infrastructure by Operating Transnational Cyber-Physical Brewing Labs

Deuse, J

Wöstmann, R Syberg, M West, N Wagstyl, D Moreno, VH

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Publisher:: Springer Nature
Publication Type:: Chapter
Citation:: Learning Factories of the Future, 2024, 1059 LNNS, pp. 171-178
Issue Date:: 2024-01-01

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Field	Value	Language
dc.contributor.author	Deuse, J https://orcid.org/0000-0003-4066-4357
dc.contributor.author	Wöstmann, R
dc.contributor.author	Syberg, M
dc.contributor.author	West, N
dc.contributor.author	Wagstyl, D
dc.contributor.author	Moreno, VH
dc.date.accessioned	2024-09-06T04:22:38Z
dc.date.available	2024-09-06T04:22:38Z
dc.date.issued	2024-01-01
dc.identifier.citation	Learning Factories of the Future, 2024, 1059 LNNS, pp. 171-178
dc.identifier.isbn	9783031654107
dc.identifier.uri	http://hdl.handle.net/10453/180729
dc.description.abstract	The convergence of information technology with production technology, coupled with the escalating intricacy of production processes and products, is leading to new demands on the education of future engineers in mechanical engineering and related disciplines. New job profiles are arising and require interdisciplinary cooperation in the fields of information technology and the Internet of Things, machine learning and domain knowledge in order to enable data-based decisions for monitoring and improving products and processes in industrial production. The core of competence building has a strong focus on applying theoretical knowledge and information in order to make it tangible and thus enable people to learn. The contribution shows the conception and implementation of transnationally connected cyber-physical brewing labs, which were set up as learning factories for students and industry partners at TU Dortmund University and the University of Technology Sydney. The focus is on Industry 4.0 technologies, such as shared data space, condition monitoring of machines and assets in the Internet of Things, and the application of machine learning for product and process optimization. This article discusses the derivation of competence profiles, roles and the development of targeted theoretical and practical learning modules. It provides an overview of the use in various formats at both sites. The evolution of the brewing labs in existing research activities is also discussed. Finally, an outlook on future activities is given.
dc.language	en
dc.publisher	Springer Nature
dc.relation.ispartof	Learning Factories of the Future
dc.relation.ispartofseries	Lecture Notes in Networks and Systems
dc.relation.isbasedon	10.1007/978-3-031-65411-4_21
dc.rights	info:eu-repo/semantics/closedAccess
dc.title	Establishing a Machine Learning and Internet of Things Learning Infrastructure by Operating Transnational Cyber-Physical Brewing Labs
dc.type	Chapter
utslib.citation.volume	1059 LNNS
pubs.organisational-group	University of Technology Sydney
pubs.organisational-group	University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	University of Technology Sydney/Faculty of Engineering and Information Technology/School of Mechanical and Mechatronic Engineering
pubs.organisational-group	University of Technology Sydney/Strength - CAM - Centre for Advanced Manufacturing
pubs.organisational-group	University of Technology Sydney/All Manual Groups
pubs.organisational-group	University of Technology Sydney/All Manual Groups/Centre for Advanced Manufacturing (CAM)
utslib.copyright.status	closed_access	*
dc.date.updated	2024-09-06T04:22:36Z
pubs.publication-status	Published
pubs.volume	1059 LNNS

Abstract:

The convergence of information technology with production technology, coupled with the escalating intricacy of production processes and products, is leading to new demands on the education of future engineers in mechanical engineering and related disciplines. New job profiles are arising and require interdisciplinary cooperation in the fields of information technology and the Internet of Things, machine learning and domain knowledge in order to enable data-based decisions for monitoring and improving products and processes in industrial production. The core of competence building has a strong focus on applying theoretical knowledge and information in order to make it tangible and thus enable people to learn. The contribution shows the conception and implementation of transnationally connected cyber-physical brewing labs, which were set up as learning factories for students and industry partners at TU Dortmund University and the University of Technology Sydney. The focus is on Industry 4.0 technologies, such as shared data space, condition monitoring of machines and assets in the Internet of Things, and the application of machine learning for product and process optimization. This article discusses the derivation of competence profiles, roles and the development of targeted theoretical and practical learning modules. It provides an overview of the use in various formats at both sites. The evolution of the brewing labs in existing research activities is also discussed. Finally, an outlook on future activities is given.

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http://hdl.handle.net/10453/180729