This article explores the transformative potential of learning factories in mechatronic systems development. Learning factories offer a dynamic, collaborative environment that bridges the gap between academia and industry, creating a mutually beneficial ecosystem. The LEONARDO project aims to develop innovative teaching methods, materials and tools for human-centric industrial engineering and management education leveraging on an industry 5.0 replica of a brewing system. Brewing as a process can be considered as highly complex, while brewing as a procedure serves as a ‘sexy vehicle’ for appealing student’s interest in industry 5.0 applications and human-centric production. The brewing process is and will increasingly be more automated and highly supervised. For the latter, modern implementations of sensors such as electronic nose, electronic tongue, and infrared spectroscopy are required to be installed on the brewing equipment. To efficiently use the sensor outputs, the produced signals need to be merged locally and pro-cessed adequately, researched and investigated deeply by the authors up-front with the results to be summarized. Furthermore, to enable the physical bridging of various involved institutions across Europe, connecting the relevant sites virtually presents another technological challenge. Adequate IoT equipment needs to be selected and included in the whole setup as well. Furthermore, an emphasis needs to be made on the human-centric approach, as well as data visualization. Each of the aforementioned pieces of technology need a thorough investigation along with a decent focus in integrating the puzzle pieces into the big picture which is the brewing plant. In this paper we describe the interaction along with the system integration strategies of the listed fields to enable a future proof industry 5.0 ready brewing plant, focusing on the human-centric approach demanded in the industry5.0 feature description.

Development and case study of an Industry 5.0 ready human-centric related brewing plant

K. Manfredi;A. Padovano;M. Cardamone;
2024-01-01

Abstract

This article explores the transformative potential of learning factories in mechatronic systems development. Learning factories offer a dynamic, collaborative environment that bridges the gap between academia and industry, creating a mutually beneficial ecosystem. The LEONARDO project aims to develop innovative teaching methods, materials and tools for human-centric industrial engineering and management education leveraging on an industry 5.0 replica of a brewing system. Brewing as a process can be considered as highly complex, while brewing as a procedure serves as a ‘sexy vehicle’ for appealing student’s interest in industry 5.0 applications and human-centric production. The brewing process is and will increasingly be more automated and highly supervised. For the latter, modern implementations of sensors such as electronic nose, electronic tongue, and infrared spectroscopy are required to be installed on the brewing equipment. To efficiently use the sensor outputs, the produced signals need to be merged locally and pro-cessed adequately, researched and investigated deeply by the authors up-front with the results to be summarized. Furthermore, to enable the physical bridging of various involved institutions across Europe, connecting the relevant sites virtually presents another technological challenge. Adequate IoT equipment needs to be selected and included in the whole setup as well. Furthermore, an emphasis needs to be made on the human-centric approach, as well as data visualization. Each of the aforementioned pieces of technology need a thorough investigation along with a decent focus in integrating the puzzle pieces into the big picture which is the brewing plant. In this paper we describe the interaction along with the system integration strategies of the listed fields to enable a future proof industry 5.0 ready brewing plant, focusing on the human-centric approach demanded in the industry5.0 feature description.
2024
digital factory
gamification
human centricity
industry 5.0
smart manufacturing
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/20.500.11770/399134
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