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A Framework to Collect and Reuse Engineering Knowledge in the Context of Design for Additive Manufacturing

Published online by Cambridge University Press:  26 May 2022

G. Formentini ,
C. Favi ,
M. Mandolini  and
M. Germani
G. Formentini*
Affiliation:
University of Parma, Italy
C. Favi
Affiliation:
University of Parma, Italy
M. Mandolini
Affiliation:
Università Politecnica delle Marche, Italy
M. Germani
Affiliation:
Università Politecnica delle Marche, Italy
*
giovanni.formentini@unipr.it

Abstract

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Design for AM (DfAM) requires the definition of Design Actions (DAs) to optimize AM manufacturing processes. However, AM understanding is still very blurred. Often designers are challenged by selecting the right design parameters. A method to list and collect DfAM DAs is currently missing. The paper aims at providing a framework to collect DfAM DAs according to a developed ontology to create databases (DBs). DBs were tested with two real case studies and geometric features to improve identified. Future developments aim at widening the database to provide all-around support for AM processes.

Keywords

design for additive manufacturing (DfAM)ontologyknowledge-based engineering (KBE)computer-aided design (CAD)
Type
Article
Information
Proceedings of the Design Society , Volume 2: DESIGN2022 , May 2022 , pp. 1371 - 1380
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), 2022.

References

Bose, S., Vahabzadeh, S., & Bandyopadhyay, A. (2013). Bone tissue engineering using 3D printing. Materials today, 16(12), 496–504.CrossRefGoogle Scholar
Chandrasegaran, S. K., Ramani, K., Sriram, R. D., Horváth, I., Bernard, A., Harik, R. F., & Gao, W. (2013). The evolution, challenges, and future of knowledge representation in product design systems. Computer-aided design, 45(2), 204–228.CrossRefGoogle Scholar
Chandrasekaran, B., Josephson, J. R., & Benjamins, V. R. (1999). What are ontologies, and why do we need them?. IEEE Intelligent Systems and their applications, 14(1), 20–26.CrossRefGoogle Scholar
Chu, C., Graf, G., & Rosen, D. W. (2008). Design for additive manufacturing of cellular structures. Computer-Aided Design and Applications, 5(5), 686–696.CrossRefGoogle Scholar
Eddy, D., Krishnamurty, S., Grosse, I., Perham, M., Wileden, J., & Ameri, F. (2015). Knowledge management with an intelligent tool for additive manufacturing. In International Design Engineering Technical Conferences and Computers and Information in Engineering Conference (Vol. 57045, p. V01AT02A023). American Society of Mechanical Engineers.CrossRefGoogle Scholar
Favi, C., Mandolini, M., Campi, F., Cicconi, P., & Germani, M. (2021) Design for additive manufacturing: a framework to collect and reuse engineering knowledge towards a CAD-based tool. In Proceedings of the ASME 2021 International Mechanical Engineering Congress and Exposition IMECE2021.CrossRefGoogle Scholar
Gao, W., Zhang, Y., Ramanujan, D., Ramani, K., Chen, Y., Williams, C. B., & Zavattieri, P. D.. (2015). The status, challenges, and future of additive manufacturing in engineering. Computer-Aided Design, 69, 65–89.CrossRefGoogle Scholar
Garzaniti, N., Golkar, A., & Maggiore, P. (2019). Additive Manufacturing Evaluation Tool for Design Studies. IEEE Systems Journal, 14(3), 4382–4393.Google Scholar
Gibson, I., Goenka, G., Narasimhan, R., & Bhat, N. (2010, August). Design rules for additive manufacture. In Solid Freeform Fabrication Symposium (pp. 705–716). Austin, TX: University Of Texas.Google Scholar
Gillespie, L. K. (2017). Design for Advanced Manufacturing: Technologies and Processes. McGraw-Hill Education.Google Scholar
Gruber, T. R. (1995). Toward principles for the design of ontologies used for knowledge sharing?. International journal of human-computer studies, 43(5-6), 907–928.CrossRefGoogle Scholar
Hagedorn, T. J., Krishnamurty, S., & Grosse, I. R. (2018). A knowledge-based method for innovative design for additive manufacturing supported by modular ontologies. Journal of Computing and Information Science in Engineering, 18(2).CrossRefGoogle Scholar
Hague, R., Mansour, S., & Saleh, N. (2004). Material and design considerations for rapid manufacturing. International Journal of Production Research, 42(22), 4691–4708.CrossRefGoogle Scholar
Hanzl, P., Zetek, M., Bakša, T., & Kroupa, T. (2015). The influence of processing parameters on the mechanical properties of SLM parts. Procedia Engineering, 100, 1405–1413.CrossRefGoogle Scholar
Leary, M., Merli, L., Torti, F., Mazur, M., & Brandt, M. (2014). Optimal topology for additive manufacture: A method for enabling additive manufacture of support-free optimal structures. Materials & Design, 63, 678–690.Google Scholar
Liu, J. (2016). Guidelines for AM part consolidation. Virtual and Physical Prototyping, 11(2), 133–141.CrossRefGoogle Scholar
Lu, Y., Yang, Z., Eddy, D., & Krishnamurty, S. (2018, August). Self-improving additive manufacturing knowledge management. In International Design Engineering Technical Conferences and Computers and Information in Engineering Conference (Vol. 51739, p. V01BT02A016). American Society of Mechanical Engineers.Google Scholar
Nasr, E. A., & Kamrani, A. K. (2007). Computer based design and manufacturing. Springer Science & Business Media.Google Scholar
Ngo, T. D., Kashani, A., Imbalzano, G., Nguyen, K. T., & Hui, D. (2018). Additive manufacturing (3D printing): A review of materials, methods, applications and challenges. Composites Part B: Engineering, 143, 172–196.CrossRefGoogle Scholar
Sanfilippo, E. M., Belkadi, F., & Bernard, A. (2019). Ontology-based knowledge representation for additive manufacturing. Computers in Industry, 109, 182–194.CrossRefGoogle Scholar
Vaneker, T., Bernard, A., Moroni, G., Gibson, I., & Zhang, Y. (2020). Design for additive manufacturing: Framework and methodology. CIRP Annals, 69(2), 578–599.Google Scholar
Wang, X., Xu, S., Zhou, S., Xu, W., Leary, M., Choong, P., & Xie, Y. M. (2016). Topological design and additive manufacturing of porous metals for bone scaffolds and orthopaedic implants: A review. Biomaterials, 83, 127–141.CrossRefGoogle ScholarPubMed