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DESIGN TEACHING INTEGRATING ADDITIVE MANUFACTURING CONSTRAINTS

Published online by Cambridge University Press:  19 June 2023

Robin Kromer  and
Elise Gruhier
Robin Kromer*
Affiliation:
University of Bordeaux,CNRS, Arts et Metiers Science and Technology, Bordeaux INP, I2M Bordeaux, Esplanade des Arts et Metiers, 33405 Talence, France
Elise Gruhier
Affiliation:
CNRS, University of Bordeaux, Bordeaux INP, I2M Bordeaux, Esplanade des Arts et Metiers, 33405 Talence, France
*
Kromer, Robin, I2M, France, robin.kromer@u-bordeaux.fr

Abstract

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Additive manufacturing (AM) processes are now integrated in industry. Therefore, new methods to design AM parts taken into consideration capabilities and limitations are necessary. It is very difficult for teachers to effectively guide students with ideas emerging from generative design tools. AM requires significant preparation and compromises. Topological optimization is also used depending on requirements. A significant impact on the final part quality is related to the part orientation and geometric dimensions. Therefore, this white paper focuses on detailed design steps to prepare future technicians and engineers to design for additive manufacturing. Active teaching pedagogy guideline is proposed. Students have to think in 3D and use analysis tools to create and validate the optimised design. They use immersive tools to review constraints and model diagnostic algorithm to generate data. Present approaches with design guidelines and tools enable to create AM rules based on it. Questionnaire shows that students need explicit knowledge information. Features recognition and geometry diagnostic are mandatory for complex model. Immersive tool helps to evaluate post-processing. They can now relate AM product-process relationship.

Keywords

Additive ManufacturingRequirementsDecision makingTrainingVisualisation
Type
Article
Information
Proceedings of the Design Society , Volume 3: ICED23 , July 2023 , pp. 1327 - 1336
Creative Commons
Creative Common License - CCCreative Common License - BYCreative Common License - NCCreative Common License - ND
This is an Open Access article, distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives licence (http://creativecommons.org/licenses/by-nc-nd/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is unaltered and is properly cited. The written permission of Cambridge University Press must be obtained for commercial re-use or in order to create a derivative work.
Copyright
The Author(s), 2023. Published by Cambridge University Press

References

Aalborg, PBL, Aalborg Universitet. (2015).Google Scholar
Abulrub, A.G., Attridge, A., & Williams, M.A. (2011). Virtual Reality in Engineering Education: The future of Creative Learning. International Conference IEEE EDUCON. Princess Sumaya University of Technology in Amman, Jordan.CrossRefGoogle Scholar
Burdea, G.C., & Coiffet, P. (2017). Virtual Reality Technology: Edition 2. John Wiley & Sons.Google Scholar
Castronovo, F., Nikolic, D., Mastrolembo Ventura, S., Shroff, V., Nguyen, A., Dinh, N.H.P., Yilmaz, S., Akhavian, R., & Gaedicke, C. (2019). Design and Development of a Virtual Reality Educational Game for Architectural and Construction Reviews. 126th American Society for Engineering Education Annual Conference and Exposition. June 15-19, Florida.CrossRefGoogle Scholar
Cajal, C., Santolaria, J., Velazquez, J., Aguado, S., & Albajez, J. (2013). Volumetric error compensation technique for 3D printers. Procedia Engineering, 63, 642–649, doi.org/10.1016/j.proeng.2013.08.276CrossRefGoogle Scholar
Chen, J. C., Tan, J. T. M., & Wong, Y. S. (2017). Exploring design-for-additive manufacturing knowledge and skills. International Journal of Technology and Design Education, 27(2), 233–247, doi.org/10.1080/21650349.2021.1951359Google Scholar
Douin, C., Gruhier, E., Kromer, R., Christmann, O., & Perry, N. (2022). A method for design for additive manufacturing rules formulation through spatio-temporal process discretization. 32nd CIRP Design Conference, Elsevier.CrossRefGoogle Scholar
Gibson, I., Rosen, D.W., & Stucker, B. (2015). Design for additive manufacturing. Addit. Manuf. Technol. https://doi.org/10.1007/978-1-4939-2113-3_17.CrossRefGoogle Scholar
Halabi, O. (2020) Immersive virtual reality to enforce teaching in engineering education. Multimedia Tools and Applications, 79, pp. 2987–3004, doi.org/10.10.1007/s11042-019-08214-8CrossRefGoogle Scholar
Innocenti, E.D., Geronazzo, M., Vescovi, D., Nordahl, R., Serafin, S., Ludovico, L.A., & Avanzini, F. (2019). Mobile virtual reality for musical genre learning in primary education. Computers & Education, 139, 102–117, doi.org/10.1016/j.compedu.2019.04.010CrossRefGoogle Scholar
Mathur, A.S. (2015). Low cost virtual reality for medical training. IEEE Virtual Reality. March 23rd-27th, France.CrossRefGoogle Scholar
Mokhtarian, H., & He, X. (2020). Design for Additive Manufacturing (DfAM): Trends, opportunities, considerations, and constraints. Journal of Manufacturing and Materials Processing, 4(1), 22, 10.1016/j.cirp.2016.05.004Google Scholar
Mbow, Mouhamadou Mansour, Grandvallet, Christelle, Vignat, Frédéric, Marin, Philippe René, Perry, Nicolas, (2021). Mathematization of experts knowledge: example of part orientation in additive manufacturing. Journal of Intelligent Manufacturing, pp.1–19, https://dx.doi.org/10.1007/s10845-020-01719-2CrossRefGoogle Scholar
Rebaioli, L., & Fassi, I. (2017). A review on benchmark artifacts for evaluating the geometrical performance of additive manufacturing processes. International Journal of Advanced Manufacturing Technology, 93, 2571–2598, doi.org/10.1007/s00170-017-0570-0CrossRefGoogle Scholar
Rezaei, S., & Lovorn, M. (2022). Data-Driven Decision Making in Education: Opportunities and Challenges. Journal of Educational Technology Development and Exchange (JETDE), 14(1), 1–22, doi.org/10.1007/978-94-007-4816-3Google Scholar
Sotomayor, Sbrugnera, Caiazzo, N.A., Alfieri, F., V. Enhancing Design for Additive Manufacturing Workflow: Optimization, Design and Simulation Tools. Appl. Sci. 2021, 11, 6628. https://doi.org/10.3390/app11146628Google Scholar
Wolfartsberger, J. (2019). Analyzing the potential of virtual reality for engineering design review. Automation in Construction, 104, 27–37. https://doi.org/10.1016/j.autcon.2019.03.007CrossRefGoogle Scholar