Hostname: page-component-586b7cd67f-dsjbd Total loading time: 0 Render date: 2024-11-30T23:30:32.197Z Has data issue: false hasContentIssue false
  • English
  • Français

KNOWLEDGE-DRIVEN DESIGN FOR ADDITIVE MANUFACTURING: A FRAMEWORK FOR DESIGN ADAPTATION

Published online by Cambridge University Press:  19 June 2023

Paul Schaechtl ,
Stefan Goetz ,
Benjamin Schleich  and
Sandro Wartzack
Paul Schaechtl*
Affiliation:
Friedrich-Alexander-Universität Erlangen-Nürnberg;
Stefan Goetz
Affiliation:
Friedrich-Alexander-Universität Erlangen-Nürnberg;
Benjamin Schleich
Affiliation:
Technische Universität Darmstadt
Sandro Wartzack
Affiliation:
Friedrich-Alexander-Universität Erlangen-Nürnberg;
*
Schaechtl, Paul, Friedrich-Alexander-Universität Erlangen-Nürnberg, Germany, [email protected]

Abstract

Core share and HTML view are not available for this content. However, as you have access to this content, a full PDF is available via the ‘Save PDF’ action button.

Due to the high freedom of design, additive manufacturing (AM) is increasingly substituting conventional manufacturing technology in several sectors. However, the knowledge and the awareness for the suitable design of additively manufactured components or assemblies ensuring manufacturability and fully realizing its potential is still lacking. In recent years, approaches and tools have emerged that allow the incorporation of existing knowledge of Design for Additive Manufacturing (DfAM) into the design process. Nevertheless, these applications mostly do not consider the formalisation of both restrictive and opportunistic DfAM guidelines for their integration in design tools.

Therefore, the following article presents a framework for the knowledge-driven adaptation of existing designs in the context of DfAM within an expert system. The novelty of the presented approach lies in the interdisciplinarity between the formalization of design guidelines and their integration and consideration within computeraided design for the semi-automated adaptation of functional non-assembly mechanisms. The application of the presented framework to a case study manufactured via Fused Layer Modeling (FLM) illustrates the applicability and benefits.

Keywords

Additive ManufacturingKnowledge-driven DesignDesign for Additive Manufacturing (DfAM)Ontologies
Type
Article
Information
Proceedings of the Design Society , Volume 3: ICED23 , July 2023 , pp. 2405 - 2414
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

Biedermann, M., Beutler, P. and Meboldt, M. (2021), “Automated design of additive manufactured flow components with consideration of overhang constraint”, Additive Manufacturing, Vol. 46, p. 102119, http://doi.org/10.1016/j.addma.2021.102119.CrossRefGoogle Scholar
Biedermann, M., Beutler, P. and Meboldt, M. (2022), “Automated Knowledge-Based Design for Additive Manufacturing: A Case Study with Flow Manifolds”, Chemie Ingenieur Technik, Vol. 94 No. 7, pp. 939–947, http://doi.org/10.1002/cite.202100209.CrossRefGoogle Scholar
Booth, J.W., Alperovich, J., Chawla, P., Ma, J., Reid, T.N. and Ramani, K. (2017), “The Design for Additive Manufacturing Worksheet”, Journal of Mechanical Design, Vol. 139 No. 10, p. 100904, http://doi.org/10.1115/1.4037251.CrossRefGoogle Scholar
Chandrasegaran, S.K., Ramani, K., Sriram, R.D., Horvath, I., Bernard, A., Harik, R.F. and Gao, W. (2013), “The evolution, challenges, and future of knowledge representation in product design systems”, Computer-Aided Design, Vol. 45 No. 2, pp. 204–228, http://doi.org/10.1016/jxad.2012.08.006.CrossRefGoogle Scholar
Consortium, M. (2001), Managing Engineering Knowledge: MOKA: Methodology for Knowledge Based Engineering Applications, London Bury St. Edmunds.Google Scholar
Cuellar, J.S., Smit, G., Zadpoor, A.A. and Breedveld, P. (2018), “Ten guidelines for the design of non-assembly mechanisms: The case of 3D-printed prosthetic hands”, Proc Inst Mech EngH, Vol. 232 No. 9, pp. 962–971, http://doi.org/10.1177/0954411918794734.CrossRefGoogle ScholarPubMed
Dinar, M. and Rosen, D.W. (2017), “A Design for Additive Manufacturing Ontology”, Journal of Computing and Information Science in Engineering, Vol. 17 No. 2, p. 021013, http://doi.org/10.1115/L4035787.CrossRefGoogle Scholar
Eddy, D., Krishnamurty, S., Grosse, I., Perham, M., Wileden, J. and Ameri, F. (2015), “Knowledge Management With an Intelligent Tool for Additive Manufacturing”, in: Volume 1A: 35th Computers and Information in Engineering Conference, American Society of Mechanical Engineers, Boston, Massachusetts, USA, p. V01AT02A023, http://doi.org/10.1115/DETC2015-46615.Google Scholar
Formentini, G., Favi, C., Mandolini, M. and Germani, M. (2022), “A Framework to Collect and Reuse Engineering Knowledge in the Context of Design for Additive Manufacturing”, Proc. Des. Soc., Vol. 2, pp. 1371–1380, http://doi.org/10.1017/pds.2022.139.CrossRefGoogle Scholar
Goetz, S. and Schleich, B. (2020), “Ontology-based representation of tolerancing and design knowledge for an automated tolerance specification of product concepts”, Procedia CIRP, Vol. 92, pp. 194–199, http://doi.org/10.1016/j.procir.2020.03.128.CrossRefGoogle Scholar
Gruber, T.R. (1995), “Toward principles for the design of ontologies used for knowledge sharing?”, International Journal of Human-Computer Studies, Vol. 43 No. 5-6, pp. 907–928, http://doi.org/10.1006/ijhc.1995.1081.CrossRefGoogle Scholar
Hagedorn, T.J., Krishnamurty, S. and 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, Vol. 18 No. 2, p. 021009, http://doi.org/10.1115/L4039455.CrossRefGoogle Scholar
Jee, H. and Witherell, P. (2017), “A method for modularity in design rules for additive manufacturing”, RPJ, Vol. 23 No. 6, pp. 1107–1118, http://doi.org/10.1108/RPJ-02-2016-0016.CrossRefGoogle Scholar
Kim, S., Rosen, D.W., Witherell, P. and Ko, H. (2019), “A Design for Additive Manufacturing Ontology to Support Manufacturability Analysis”, Journal of Computing and Information Science in Engineering, Vol. 19 No. 4, p. 041014, http://doi.org/10.1115/L4043531.CrossRefGoogle Scholar
Ko, H., Witherell, P., Lu, Y., Kim, S. and Rosen, D.W. (2021), “Machine learning and knowledge graph based design rule construction for additive manufacturing”, Additive Manufacturing, Vol. 37, p. 101620, http://doi.org/10.1016/j.addma.2020.101620.CrossRefGoogle Scholar
Kumke, M. (2018), Methodisches Konstruieren von additiv gefertigten Bauteilen, No. Band 124 in AutoUni — Schriftenreihe, Springer, Wiesbaden.CrossRefGoogle Scholar
Lamy, J.B. (2017), “Owlready: Ontology-oriented programming in Python with automatic classification and high level constructs for biomedical ontologies”, Artificial Intelligence in Medicine, Vol. 80, pp. 11–28, http://doi.org/10.1016/j.artmed.2017.07.002.CrossRefGoogle ScholarPubMed
Laverne, F., Segonds, F., Anwer, N. and Le Coq, M. (2015), “Assembly Based Methods to Support Product Innovation in Design for Additive Manufacturing: An Exploratory Case Study”, Journal of Mechanical Design, Vol. 137 No. 12, p. 121701, http://doi.org/10.1115/L4031589.CrossRefGoogle Scholar
Lussenburg, K., Sakes, A. and Breedveld, P. (2021), “Design of non-assembly mechanisms: A state-of-the-art review”, Additive Manufacturing, Vol. 39, p. 101846, http://doi.org/10.1016/jj.addma.2021.101846.CrossRefGoogle Scholar
Mayerhofer, M., Lepuschitz, W., Hoebert, T., Merdan, M., Schwentenwein, M. and Strasser, T.I. (2021), “Knowledge-Driven Manufacturability Analysis for Additive Manufacturing”, IEEE Open J. Ind. Electron. Soc., Vol. 2, pp. 207–223, http://doi.org/10.1109/0JIES.2021.3061610.CrossRefGoogle Scholar
Musen, M.A. (2015), “The protege project: A look back and a look forward”, AIMatters, Vol. 1 No. 4, pp. 4–12, http://doi.org/10.1145/2757001.2757003.CrossRefGoogle Scholar
Noy, N. and McGuiness, D. (2001), “Ontology Development 101: A Guide to Creating Your First Ontology”,.Google Scholar
Pradel, P., Zhu, Z., Bibb, R. and Moultrie, J. (2018), “Investigation of design for additive manufacturing in professional design practice”, Journal of Engineering Design, Vol. 29 No. 4-5, pp. 165–200, http://doi.org/10.1080/09544828.2018.1454589.CrossRefGoogle Scholar
Qi, Q., Pagani, L., Scott, P.J. and Jiang, X. (2018), “A categorical framework for formalising knowledge in additive manufacturing”, Procedia CIRP, Vol. 75, pp. 87–91, http://doi.org/10.1016/jj.procir.2018.04.076.CrossRefGoogle Scholar
Schaechtl, P., Schleich, B. and Wartzack, S. (2021), “Statistical Tolerance Analysis of3D-Printed Non-Assembly Mechanisms in Motion Using Empirical Predictive Models”, Applied Sciences, Vol. 11 No. 4, p. 1860, http://doi.org/10.3390/app11041860.CrossRefGoogle Scholar
Schulz, S., Schlattmann, J. and Rosenthal, S. (2017), “Konstruktionsrichtlinien fur die funktionsgerechte Gestal- tung additiv gefertigter Kunststoffgelenke”, in:Stuttgarter Symposium Fur Produktentwicklung 2017, Stuttgart.Google Scholar
Sossou, G., Demoly, F., Montavon, G. and Gomes, S. (2018), “An additive manufacturing oriented design approach to mechanical assemblies”, Journal of Computational Design and Engineering, Vol. 5 No. 1, pp. 3–18, http://doi.org/10.1016/jjcde.2017.11.005.CrossRefGoogle Scholar
Storga, M., Andreasen, M.M. and Marjanovic, D. (2010), “The design ontology: Foundation for the design knowledge exchange and management”, Journal of Engineering Design, Vol. 21 No. 4, pp. 427–454, http://doi.org/10.1080/09544820802322557.CrossRefGoogle Scholar
Thompson, M.K., Moroni, G., Vaneker, T., Fadel, G., Campbell, R.I., Gibson, I., Bernard, A., Schulz, J., Graf, P., Ahuja, B. and Martina, F. (2016), “Design for Additive Manufacturing: Trends, opportunities, considerations, and constraints”, CIRP Annals, Vol. 65 No. 2, pp. 737–760, http://doi.org/10.1016/jj.cirp.2016.05.004.CrossRefGoogle Scholar
Vaneker, T., Bernard, A., Moroni, G., Gibson, I. and Zhang, Y. (2020), “Design for additive manufacturing: Framework and methodology”, CIRP Annals, Vol. 69 No. 2, pp. 578–599, http://doi.org/10.1016/jj.cirp.2020.05.006.CrossRefGoogle Scholar
Wohlers, T., Campbell, R.I., Diegel, O., Huff, R. and Kowen, J. (2020), Wohlers Report 2020, Wohlers Associates, Fort Collins, Colo.Google Scholar
Zirngibl, C., Kugler, P., Popp, J., Bielak, C.R., Bobbert, M., Drummer, D., Meschut, G., Wartzack, S. and Schleich, B. (2022), “Provision of cross-domain knowledge in mechanical joining using ontologies”, Prod. Eng. Res. Devel., Vol. 16 No. 2-3, pp. 327–338, http://doi.org/10.1007/s11740-022-01117-y.CrossRefGoogle Scholar