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Web based resources (IIC etc)

https://www.iiconsortium.org/

The Industry IoT Consortium is probably the best place to explore current efforts of advancing the knowledge and standards including: 

  • updated reference architectures (and patterns)
  • open white papers on the most important subjects/use-cases and technologies (to be discussed)
  • Foundational documents (and resources for communication)

Patterns and best practice examples, systems and models for re-use and inspiration are valuable. 

Architecture Models like the OSI model for the internet and the ISA 95 model for industrial information systems are well known patterns/theory used in the industry and current "state-of-the-art", below example (figure by Badarinath Katti, thesis, 2020):

  Figure by Badarinath Katti, thesis, 2020 (see ref. for context and background)

Education happens through systematic "Learning by doing" - and is a continuous process going well beyond a formal education- at NTNU or elsewhere. Knowing where to start is often a great challenge, and guidance may be hard to find- even though Industry 4.0 and Education 4.0 and similar concepts are everywhere on the internet and already a part of popular culture in various forms....

Experiential Learning is a foundation of modern education, not least in engineering - and "Learning Factories". The pedagogy and philosophy shares some common models with the "learning cycle" also known as the Deming cycle of Plan-Do-Study-Act (or PDCA for plan-do-check-act ) in the industry and so-called "Lean" philosophy of quality focus. Some useful video-content and more background (and quote below) on experiential learning may be found online based on the work of Kolb et al. through:

https://experientiallearninginstitute.org/resources/what-is-experiential-learning/ 

“There are two goals in the experiential learning process. One is to learn the specifics of a particular subject, and the other is to learn about one’s own learning process.”
- David A. Kolb

Learning factory examples

Model of Learning factories in context of Industry 4.0 So-called "Learning factories" have been developed over the years, and may be viewed as best-practice and "The future of Industry 4.0 Education".  A recent textbook on Learning factories by Abele et al. (Springer) is available through the link below, and e.g. the NTNU library - also as a pdf-version/eBook:

https://link.springer.com/book/10.1007/978-3-319-92261-4

The background, definitions and many examples are presented in the textbook, and related literature. Together with end-users and partners, Festo Didactic has implemented a large number of Cyber-Physical Learning Factories at NTNU and elsewhere.

In any approach to understanding complex systems, the viewpoint will determine how the system looks to an actor, or user- or learner. A view may highlight different system components, or aspects and details of any particular system function. A simple scale (bottom-up) mapping of "Human centric" vs "Machine centric" points of view may be helpful- not least to discuss the functions of the Human-Machine-Interfaces (HMI) of the system and its components. 

A conceptual model of a learning factory, and enabling technology such as AR, "Augmented Reality" and AI,  "Assisted/Artificial Intelligence" in the context of the vision of Industry 4.0 (above) was first presented in a paper from 2020 (Tvenge et al.), available at NTNU Open: 

https://ntnuopen.ntnu.no/ntnu-xmlui/handle/11250/2719708


Recent paper from Thomas Riemann et al. (TU Darmstadt, see other LinkedIn page for background):

Hybrid Learning Factories for Lean Education: Approach and Morphology for Competency-Oriented Design of Suitable Virtual Reality Learning Environments

Learning in the Digital Era : 7th European Lean Educator Conference, ELEC 2021 · Dec 31, 2021Learning in the Digital Era : 7th European Lean Educator Conference, ELEC 2021 · Dec 31, 2021

Abstract

In recent years, learning factories have proven to be an effective instrument for developing competencies, especially in lean production and digitization. The concept of learning factories has been enriched in the recent past by elements and training units in virtual reality (VR). This enrichment allows an expansion of the mapping abilities of different training environments and value streams in the context of lean education. Nevertheless, learning factory developers are faced with the challenge of selecting suitable scenarios in terms of content and scope. An approach for the competency-oriented and structured design of such scenarios will be presented in this publication and illustrated by means of an application example of the research project PortaL (Virtual action tasks for personalized, adaptive learning).


More examples and context to follow:


People-Profits-Planet (Stories and context)

This wiki-page will form a joint study of this wide topic (Industry 4.0) and education to support the smart factories of the future.

We start through a project and aim to continue after the project - through ongoing support at our places of work and life (support) at home.

More Stories and Context will be added as we continue our studies and cooperation.

What is smart, sustainable manufacturing?

In 2016, the NIST provided the following introduction and a good overview of state-of-the-art (link below): 

A manufacturer’s sustainable competitiveness depends on its capabilities with respect to cost, delivery, flexibility, and quality [1]. Smart Manufacturing Systems (SMS) attempt to maximize those capabilities by using advanced technologies that promote rapid flow and widespread use of digital information within and between manufacturing systems[2][3][4]. SMS are driving unprecedented gains in production agility, quality, and efficiency across U.S. manufacturers, improving long-term competitiveness. Specifically, SMS use information and communication technologies along with intelligent software applications to

1. Optimize the use of labor, material, and energy to produce customized, high-quality products for ontime delivery.

2. Quickly respond to changes in market demands and supply chains. 

NIST (2016): http://dx.doi.org/10.6028/NIST.IR.8107


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