No abstract

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.

Background: The Industry 4.0 wave is leading the changes in existing manufacturing and industrial processes across the world. This is especially important in the formulation of the smart-factory concept with an outlook to energy sustainable processes. In viewing and identifying the foundational elements of such a transformation, the initial conditions and current practices in a cross-sectoral manner is considered a first, yet crucial step in the EU-funded project EnerMan. Methods: In this paper, we identify and analyse the key common features and characteristics of industrial practices set in a perspective of similar and identical functions with a focus to three key energy areas: sustainability, management, and footprint. The examination of different industrial sector cases is performed via distributed questionnaires and then viewed under the prism of the equifinality state via a text-mining analysis approach. Results: identification of common themes and benchmarking of current practices in a cross-industry manner led to the creation of a common systemic framework within energy management related aspects, which is hereby presented. Conclusions: use of an equifinality approach in energy management practices should be further pursued to open up new methods of ideation and innovation and communicate systems’ design in tandem with each industrial set goals.

Additive manufacturing (AM) is a versatile technology that could add flexibility in manufacturing processes, whether implemented alone or along other technologies. This technology enables on-demand production and decentralized production networks, as production facilities can be located around the world to manufacture products closer to the final consumer (decentralized manufacturing). However, the wide adoption of additive manufacturing technologies is hindered by the lack of experience on its implementation, the lack of repeatability among different manufacturers and a lack of integrated production systems. The later, hinders the traceability and quality assurance of printed components and limits the understanding and data generation of the AM processes and parameters. In this article, a design strategy is proposed to integrate the different phases of the development process into a model-based design platform for decentralized manufacturing. This platform is aimed at facilitating data traceability and product repeatability among different AM machines. The strategy is illustrated with a case study where a car steering knuckle is manufactured in three different facilities in Sweden and Italy.