FactoryBricks: a New Learning Platform for Smart Manufacturing Systems

Manufacturing industries are facing radical changes under the technological acceleration of Industry 4.0. The manufacturing workforce is not ready for such disruptions due to the lack of vertical skills on digital technologies. Production planning and control of manufacturing systems is often an experience-based art. Further, the companies need of offering training paths for long-life learning of their employees finds several obstacles in the availability of skilled trainers and the trainee’s low engagement with traditional learning models. This paper presents how the FactoryBricks project aims at overcoming the aforementioned issues. The project delivers effective training courses to enable the uptake of industrial technologies and smart manufacturing systems for professionals, either executives or technicians. Beside digital learning contents, the learners are offered an interaction with lab-scale models of production systems built with modular components such as LEGO®. The courses are designed in a modular way, and aim to teach manufacturing concepts in three main topics: (1) the physical system and its dynamics, (2) the physical-digital data connections for smart online analytics, and (3) the exploitation of digital models for production. The paper also presents the results of the prototypical implementation of the project

A Teaching Factory knowledge exchange network

Skills and competences required by the labor market evolve at a high rate. In addition, manufacturing enterprises face a number of technical and non-technical challenges in their daily business, and most of them are relatively slow as it regards innovation adoption. Academia needs to be able to closely follow industrial needs, to generate the right kind of professionals. In addition, academia owns a lot of high-value specialized industrial equipment, which is not shared and subsequently often underutilized. Over and above, COVID-19 has significantly impacted the educational institutes' operation. All aforementioned facts point to one specific need; an effective remote collaboration paradigm aiming at knowledge exchange. The Teaching Factory paradigm provides a real-life environment for students to develop their skills and competences, through directly involving them with real-life industrial challenges. Through the use of modern digital technologies and tools, and in combination with the relevant educational approach, a two-way online knowledge communication between academia and industry is formed, aiming to mutually benefit both stakeholders. This work focuses on presenting a framework for successfully extending the established Teaching Factory paradigm on a network level, taking advantage of the unique characteristics of all aforementioned actors and connecting them together to the ecosystem benefit, forming a Teaching Factory Knowledge Exchange Network. The educational approach and required ICT infrastructure for the facilitation of knowledge exchange are presented. The proposed framework and tools applicability are validated in two heterogeneous pilot applications, using different modalities of the proposed framework, involving a collaborative academic teaching scheme via virtually interconnected classrooms and labs, as well as a collaboratively solving an industrial challenge linked with digital work instructions in manual assembly.Peer reviewe

FactoryBricks : une nouvelle plateforme d'apprentissage pour les systèmes de fabrication intelligents

International audienceManufacturing industries are facing radical changes under the technological acceleration of Industry 4.0. The manufacturing workforce is not ready for such disruptions due to the lack of vertical skills on digital technologies. Production planning and control of manufacturing systems is often an experience-based art. Further, the companies need of offering training paths for long-life learning of their employees finds several obstacles in the availability of skilled trainers and the trainee's low engagement with traditional learning models. This paper presents how the FactoryBricks project aims at overcoming the aforementioned issues. The project delivers effective training courses to enable the uptake of industrial technologies and smart manufacturing systems for professionals, either executives or technicians. Beside digital learning contents, the learners are offered an interaction with lab-scale models of production systems built with modular components such as LEGO ®. The courses are designed in a modular way, and aim to teach manufacturing concepts in three main topics: (1) the physical system and its dynamics, (2) the physical-digital data connections for smart online analytics, and (3) the exploitation of digital models for production. The paper also presents the results of the prototypical implementation of the project.Les industries manufacturières sont confrontées à des changements radicaux sous l'accélération technologique de l'industrie 4.0. La main-d'œuvre manufacturière n'est pas prête pour ces bouleversements en raison du manque de compétences verticales sur les technologies numériques. La planification et le contrôle de la production des systèmes de fabrication sont souvent un art basé sur l'expérience. En outre, les entreprises qui ont besoin d'offrir des parcours de formation pour l'apprentissage tout au long de la vie de leurs employés rencontrent plusieurs obstacles dans la disponibilité de formateurs qualifiés et le faible engagement des stagiaires dans les modèles d'apprentissage traditionnels. Cet article présente comment le projet FactoryBricks vise à surmonter les problèmes susmentionnés. Le projet propose des cours de formation efficaces pour permettre l'adoption des technologies industrielles et des systèmes de fabrication intelligents par les professionnels, qu'ils soient cadres ou techniciens. Outre les contenus d'apprentissage numériques, les apprenants ont la possibilité d'interagir avec des modèles à l'échelle du laboratoire de systèmes de production construits avec des composants modulaires tels que LEGO®. Les cours sont conçus de manière modulaire et visent à enseigner les concepts de fabrication dans trois domaines principaux : (1) le système physique et sa dynamique, (2) les connexions entre les données physiques et numériques pour une analyse intelligente en ligne, et (3) l'exploitation des modèles numériques pour la production. L'article présente également les résultats de la mise en œuvre prototypique du projet

Evaluation neuer Lehr-Lern-Medien in einer Lernfabrik. Eine Usability-Studie zu App- und AR-Anwendungen

Die Technische Universität Braunschweig verfügt über eine Lernfabrik. Ein Teil dieser ist die sogenannte Experimentierfabrik, welche als herunterskalierte, modular aufgebaute, verkettete Produktion gestaltet ist. An dieser arbeiten Studierende in Kleingruppen vorlesungsbegleitend eigenständig an Forschungsfragen. Künftig sollen die Studierenden mithilfe digitaler Medien mit der Experimentierfabrik und mit der Fabrikhalle vertraut gemacht, in sicherheitsrelevante Aspekte eingeführt und durch verschiedene, komplexer werdende Aufgabenstellungen zur Energie- und Ressourceneffizienz geführt werden. Hierzu wurden eine Tablet-App und eine Augmented-Reality-Anwendung (AR) entwickelt. Zum Zeitpunkt der Untersuchung waren diese in der Entwicklung und sollten iterativ verbessert werden. Das primäre Ziel der vorliegenden Studie ist die Ableitung von Empfehlungen zur Gestaltung und inhaltlichen Optimierung der zwei entwickelten Lehr-Lern-Systeme zum erfolgreichen Einsatz dieser in der Hochschullehre. Nachfolgend werden nur jene Instrumente berichtet, die zur Untersuchung der Usability eingesetzt wurden. (DIPF/Orig.

Mirrorlabs - creating accessible Digital Twins of robotic production environment with Mixed Reality

How to visualize recorded production data in Virtual Reality? How to use state of the art Augmented Reality displays that can show robot data? This paper introduces an open-source ICT framework approach for combining Unity-based Mixed Reality applications with robotic production equipment using ROS Industrial. This publication gives details on the implementation and demonstrates the use as a data analysis tool in the context of scientific exchange within the area of Mixed Reality enabled human-robot co-production.Peer reviewe