Implementation of a Six-Layer Smart Factory Architecture with Special Focus on Transdisciplinary Engineering Education

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Implementation of a Six-Layer Smart Factory Architecture with Special Focus on Transdisciplinary Engineering Education. / Ralph, Benjamin James; Sorger, Marcel; Schödinger, Benjamin et al.
In: Sensors, Vol. 21.2021, No. 9, 2944, 22.04.2021.

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@article{b4984b5d84d04963baef39ce804d43bd,
title = "Implementation of a Six-Layer Smart Factory Architecture with Special Focus on Transdisciplinary Engineering Education",
abstract = "Smart factories are an integral element of the manufacturing infrastructure in the context of the fourth industrial revolution. Nevertheless, there is frequently a deficiency of adequate training facilities for future engineering experts in the academic environment. For this reason, this paper describes the development and implementation of two different layer architectures for the metal processing environment. The first architecture is based on low-cost but resilient devices, allowing interested parties to work with mostly open-source interfaces and standard back-end programming environments. Additionally, one proprietary and two open-source graphical user interfaces (GUIs) were developed. Those interfaces can be adapted front-end as well as back-end, ensuring a holistic comprehension of their capabilities and limits. As a result, a six-layer architecture, from digitization to an interactive project management tool, was designed and implemented in the practical workflow at the academic institution. To take the complexity of thermo-mechanical processing in the metal processing field into account, an alternative layer, connected with the thermo-mechanical treatment simulator Gleeble 3800, was designed. This framework is capable of transferring sensor data with high frequency, enabling data collection for the numerical simulation of complex material behavior under high temperature processing. Finally, the possibility of connecting both systems by using open-source software packages is demonstrated.",
keywords = "Digitalization, Engineering education, Industry 4.0, Layer architecture, Metal processing, Smart factory",
author = "Ralph, {Benjamin James} and Marcel Sorger and Benjamin Sch{\"o}dinger and Hans-J{\"o}rg Schm{\"o}lzer and Karin Hartl and Martin Stockinger",
note = "Publisher Copyright: {\textcopyright} 2021 by the authors. Licensee MDPI, Basel, Switzerland.",
year = "2021",
month = apr,
day = "22",
doi = "10.3390/s21092944",
language = "English",
volume = "21.2021",
journal = "Sensors",
issn = "1424-8220",
publisher = "Multidisciplinary Digital Publishing Institute (MDPI)",
number = "9",

}

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TY - JOUR

T1 - Implementation of a Six-Layer Smart Factory Architecture with Special Focus on Transdisciplinary Engineering Education

AU - Ralph, Benjamin James

AU - Sorger, Marcel

AU - Schödinger, Benjamin

AU - Schmölzer, Hans-Jörg

AU - Hartl, Karin

AU - Stockinger, Martin

N1 - Publisher Copyright: © 2021 by the authors. Licensee MDPI, Basel, Switzerland.

PY - 2021/4/22

Y1 - 2021/4/22

N2 - Smart factories are an integral element of the manufacturing infrastructure in the context of the fourth industrial revolution. Nevertheless, there is frequently a deficiency of adequate training facilities for future engineering experts in the academic environment. For this reason, this paper describes the development and implementation of two different layer architectures for the metal processing environment. The first architecture is based on low-cost but resilient devices, allowing interested parties to work with mostly open-source interfaces and standard back-end programming environments. Additionally, one proprietary and two open-source graphical user interfaces (GUIs) were developed. Those interfaces can be adapted front-end as well as back-end, ensuring a holistic comprehension of their capabilities and limits. As a result, a six-layer architecture, from digitization to an interactive project management tool, was designed and implemented in the practical workflow at the academic institution. To take the complexity of thermo-mechanical processing in the metal processing field into account, an alternative layer, connected with the thermo-mechanical treatment simulator Gleeble 3800, was designed. This framework is capable of transferring sensor data with high frequency, enabling data collection for the numerical simulation of complex material behavior under high temperature processing. Finally, the possibility of connecting both systems by using open-source software packages is demonstrated.

AB - Smart factories are an integral element of the manufacturing infrastructure in the context of the fourth industrial revolution. Nevertheless, there is frequently a deficiency of adequate training facilities for future engineering experts in the academic environment. For this reason, this paper describes the development and implementation of two different layer architectures for the metal processing environment. The first architecture is based on low-cost but resilient devices, allowing interested parties to work with mostly open-source interfaces and standard back-end programming environments. Additionally, one proprietary and two open-source graphical user interfaces (GUIs) were developed. Those interfaces can be adapted front-end as well as back-end, ensuring a holistic comprehension of their capabilities and limits. As a result, a six-layer architecture, from digitization to an interactive project management tool, was designed and implemented in the practical workflow at the academic institution. To take the complexity of thermo-mechanical processing in the metal processing field into account, an alternative layer, connected with the thermo-mechanical treatment simulator Gleeble 3800, was designed. This framework is capable of transferring sensor data with high frequency, enabling data collection for the numerical simulation of complex material behavior under high temperature processing. Finally, the possibility of connecting both systems by using open-source software packages is demonstrated.

KW - Digitalization

KW - Engineering education

KW - Industry 4.0

KW - Layer architecture

KW - Metal processing

KW - Smart factory

UR - https://www.mdpi.com/1424-8220/21/9/2944

U2 - 10.3390/s21092944

DO - 10.3390/s21092944

M3 - Article

C2 - 33922268

VL - 21.2021

JO - Sensors

JF - Sensors

SN - 1424-8220

IS - 9

M1 - 2944

ER -