DIGITAL KNOWLEDGE TRANSFER FOR ADDITIVE MANUFACTURING USING BLENDED LEARNING

Research output: Chapter in book/report/conference proceedingConference contributionResearchpeer review

Authors

  • Sebastian Kuschmitz
  • Lukas Valentin Hoppe
  • Paul Christoph Gembarski
  • Roland Lachmayer
  • Thomas Vietor

External Research Organisations

  • Technische Universität Braunschweig
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Details

Original languageEnglish
Title of host publicationProceedings of the 24th International Conference on Engineering and Product Design Education
Subtitle of host publicationDisrupt, Innovate, Regenerate and Transform, E and PDE 2022
EditorsErik Bohemia, Lyndon Buck, Hilary Grierson
ISBN (electronic)9781912254163
Publication statusPublished - 2022
Event24th International Conference on Engineering and Product Design Education: Disrupt, Innovate, Regenerate and Transform, E and PDE 2022 - London, United Kingdom (UK)
Duration: 8 Sept 20229 Sept 2022

Abstract

Additive manufacturing (AM) processes provide new levels of design freedom during product development as a result of the layer-by-layer build-up process, so that graded lattice structures, internal cooling channels, or other geometrically distinctive design features are taken into account at an early stage of product development. In addition, these complex geometric features can be realized without significant additional effort during the additive manufacturing process while complying with the restrictions of AM. The "Design for additive manufacturing" research field is trying to offer methods and tools to support the product developer in exploiting the AM potentials and to maintain compliance with the restrictions of the manufacturing process to be able to apply these design freedoms in a targeted and benefit-oriented manner during product development. However, due to a lack of AM knowledge and limited software solutions, the application of these methods and tools is not always possible, because necessary AM knowledge is partial or even completely missing. For this reason, teaching and learning offers are needed that systematically impart specific AM knowledge so that these barriers in product development can be overcome. In this paper, the systematic knowledge acquisition for specific AM knowledge is presented using the example of interactive teaching and learning offers. For this purpose, the basics of systematic knowledge transfer for AM will be discussed first to show the state of research. This is followed by the presentation of the interactive learning environment, which makes AM-relevant topics experienceable utilizing interactive 3D models. Finally, a validation of the presented learning environment for the transfer of specific AM knowledge is presented.

Keywords

    Additive manufacturing, AM-knowledge, blended learning, design for additive manufacturing, interactive learning environment

ASJC Scopus subject areas

Cite this

DIGITAL KNOWLEDGE TRANSFER FOR ADDITIVE MANUFACTURING USING BLENDED LEARNING. / Kuschmitz, Sebastian; Hoppe, Lukas Valentin; Gembarski, Paul Christoph et al.
Proceedings of the 24th International Conference on Engineering and Product Design Education: Disrupt, Innovate, Regenerate and Transform, E and PDE 2022. ed. / Erik Bohemia; Lyndon Buck; Hilary Grierson. 2022. EPDE2022/1262.

Research output: Chapter in book/report/conference proceedingConference contributionResearchpeer review

Kuschmitz, S, Hoppe, LV, Gembarski, PC, Lachmayer, R & Vietor, T 2022, DIGITAL KNOWLEDGE TRANSFER FOR ADDITIVE MANUFACTURING USING BLENDED LEARNING. in E Bohemia, L Buck & H Grierson (eds), Proceedings of the 24th International Conference on Engineering and Product Design Education: Disrupt, Innovate, Regenerate and Transform, E and PDE 2022., EPDE2022/1262, 24th International Conference on Engineering and Product Design Education: Disrupt, Innovate, Regenerate and Transform, E and PDE 2022, London, United Kingdom (UK), 8 Sept 2022. https://doi.org/10.35199/EPDE.2022.84
Kuschmitz, S., Hoppe, L. V., Gembarski, P. C., Lachmayer, R., & Vietor, T. (2022). DIGITAL KNOWLEDGE TRANSFER FOR ADDITIVE MANUFACTURING USING BLENDED LEARNING. In E. Bohemia, L. Buck, & H. Grierson (Eds.), Proceedings of the 24th International Conference on Engineering and Product Design Education: Disrupt, Innovate, Regenerate and Transform, E and PDE 2022 Article EPDE2022/1262 https://doi.org/10.35199/EPDE.2022.84
Kuschmitz S, Hoppe LV, Gembarski PC, Lachmayer R, Vietor T. DIGITAL KNOWLEDGE TRANSFER FOR ADDITIVE MANUFACTURING USING BLENDED LEARNING. In Bohemia E, Buck L, Grierson H, editors, Proceedings of the 24th International Conference on Engineering and Product Design Education: Disrupt, Innovate, Regenerate and Transform, E and PDE 2022. 2022. EPDE2022/1262 doi: 10.35199/EPDE.2022.84
Kuschmitz, Sebastian ; Hoppe, Lukas Valentin ; Gembarski, Paul Christoph et al. / DIGITAL KNOWLEDGE TRANSFER FOR ADDITIVE MANUFACTURING USING BLENDED LEARNING. Proceedings of the 24th International Conference on Engineering and Product Design Education: Disrupt, Innovate, Regenerate and Transform, E and PDE 2022. editor / Erik Bohemia ; Lyndon Buck ; Hilary Grierson. 2022.
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abstract = "Additive manufacturing (AM) processes provide new levels of design freedom during product development as a result of the layer-by-layer build-up process, so that graded lattice structures, internal cooling channels, or other geometrically distinctive design features are taken into account at an early stage of product development. In addition, these complex geometric features can be realized without significant additional effort during the additive manufacturing process while complying with the restrictions of AM. The {"}Design for additive manufacturing{"} research field is trying to offer methods and tools to support the product developer in exploiting the AM potentials and to maintain compliance with the restrictions of the manufacturing process to be able to apply these design freedoms in a targeted and benefit-oriented manner during product development. However, due to a lack of AM knowledge and limited software solutions, the application of these methods and tools is not always possible, because necessary AM knowledge is partial or even completely missing. For this reason, teaching and learning offers are needed that systematically impart specific AM knowledge so that these barriers in product development can be overcome. In this paper, the systematic knowledge acquisition for specific AM knowledge is presented using the example of interactive teaching and learning offers. For this purpose, the basics of systematic knowledge transfer for AM will be discussed first to show the state of research. This is followed by the presentation of the interactive learning environment, which makes AM-relevant topics experienceable utilizing interactive 3D models. Finally, a validation of the presented learning environment for the transfer of specific AM knowledge is presented.",
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