Sebastian Kuschmitz, L. Hoppe, P. Gembarski, R. Lachmayer, T. Vietor
{"title":"使用混合学习的增材制造数字知识转移","authors":"Sebastian Kuschmitz, L. Hoppe, P. Gembarski, R. Lachmayer, T. Vietor","doi":"10.35199/epde.2022.84","DOIUrl":null,"url":null,"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.","PeriodicalId":147286,"journal":{"name":"DS 117: Proceedings of the 24th International Conference on Engineering and Product Design Education (E&PDE 2022), London South Bank University in London, UK. 8th - 9th September 2022","volume":"687 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"DIGITAL KNOWLEDGE TRANSFER FOR ADDITIVE MANUFACTURING USING BLENDED LEARNING\",\"authors\":\"Sebastian Kuschmitz, L. Hoppe, P. Gembarski, R. Lachmayer, T. Vietor\",\"doi\":\"10.35199/epde.2022.84\",\"DOIUrl\":null,\"url\":null,\"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. 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DIGITAL KNOWLEDGE TRANSFER FOR ADDITIVE MANUFACTURING USING BLENDED LEARNING
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.