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Automated Candidate Detection for Additive Manufacturing: A Framework Proposal

Part of: Design for Additive Manufacturing

Published online by Cambridge University Press:  26 July 2019

Thomas Daniel Page ,
Sheng Yang  and
Yaoyao Fiona Zhao

Abstract

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As additive manufacturing (AM) continues to grow in its abilities, so does the need for a quick and effective method of determining how it should be applied. Over time, these methods are naturally developed and passed on as tacit knowledge. However, with the rapid advancement of AM technologies, identifying parts which are eligible for AM as well as gaining insight on what value it may add to a product needs to be modelled in an objective and transferrable way. This paper presents a framework for determining the candidacy of a part or assembly for AM, represented by its economic feasibility and potential for AM-specific benefits. A set of selection criteria is developed with the goal of fast-screening in mind; that is specific data which can be automatically extracted from CAD models and resource planning databases. A case study is performed to validate the criteria and decision model chosen, as well as gain insight to the potential for a more widespread application. The decision model successfully identified economic feasibility and AM potentials, which suggests the results of the case study show promise for a semi-automatic decision support system for identifying AM candidates.

Keywords

Additive ManufacturingDecision makingMachine learningSelection criteria
Type
Article
Information
Proceedings of the Design Society: International Conference on Engineering Design , Volume 1 , Issue 1 , July 2019 , pp. 679 - 688
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) 2019

References

Bishop, C.M. (2006), Pattern Recognition and Machine Learning (Information Science and Statistics). Springer-Verlag, Berlin, Heidelberg. ISBN 0387310738.Google Scholar
Hastie, T., Tibshirani, R. and Friedman, J. (2001), The Elements of Statistical Learning. Springer Series in Statistics. Springer New York Inc., New York, NY, USA.Google Scholar
Huang, S.H., Dismukes, J.P., Shi, J., Su, Q., Wang, G., Razzak, M.A. and Robinson, D.E. (2002), “Manufacturing system modeling for productivity improvement”, Journal of Manufacturing Systems, Vol. 21 No. 4:249–259. ISSN 0278-6125. https://doi.org/10.1016/S0278-6125(02)80165-0Google Scholar
ISO/ASTM. (2017), “Standard guidelines for design for additive manufacturing”, Standard, ISO/ASTM.Google Scholar
Klahn, C., Leutenecker, B. and Meboldt, M. (2014), “Design for additive manufacturing âĂ Ş supporting the substitution of components in series products”, Procedia CIRP, Vol, 21, No 138 âĂ Ş 143. ISSN . https://doi.org/10.1016/j.procir.2014.03.145. 24th CIRP Design Conference.Google Scholar
Kruse, A., Reiher, T. and Koch, R. (2017), “Integrating am into existing companies - selection of existing parts for increase of acceptance”, In Proceedings of the 28th Annual International Solid Freeform Fabrication Symposium.Google Scholar
Li, Y. (2015), Manufacturability analysis for non-feature-based objects, PhD thesis, Iowa State University.Google Scholar
Lindemann, C., Reiher, T., Jahnke, U. and Koch, R. (2015), “Towards a sustainable and economic selection of part candidates for additive manufacturing”, Rapid Prototyping Journal, Vol, 21 No. 2, pp. 216–227. http://doi.org/10.1108/RPJ-12-2014-0179.Google Scholar
Lovatt, A.M and Shercliff, H.R. (1998), “Manufacturing process selection in engineering design. part 1: the role of process selection”, Materials & Design, Vol. 19 No. 5: pp. 205–215. ISSN 0261-3069. https://doi.org/10.1016/S0261-3069(98)00038-7Google Scholar
Muir, M. and Haddud, A. (2018), “Additive manufacturing in the mechanical engineering and medical industries spare parts supply chain”, Journal of Manufacturing Technology Management, Vol. 29 No. 2: pp. 372–397. http://doi.org/10.1108/JMTM-01-2017-0004Google Scholar
Reiher, T., Lindemann, C., Jahnke, U., Deppe, G. and Koch, R., (2017), “Holistic approach for industrializing am technology: from part selection to test and verification”, Progress in Additive Manufacturing, Vol. 2 No. 1:43–55, Jun. . http://doi.org/10.1007/s40964-017-0018-yGoogle Scholar
Reiher, T., Lindemann, C., Moi, M. and Koch, R. (2013), “Impact and influence factors of additive manufacturing on product lifecycle costs”, In Solid Freeform Fabrication Proceedings.Google Scholar
scikit (no date) scikit learn. Available at: https://scikit-learn.org/stable/ (Accessed: 23 February 2019).Google Scholar
Senvol, LLC, “Senvol LLC 7 scenarios table”. http://senvol.com/additive-manufacturing/7-scenarios-table/, n.d. Accessed: 2018-10-09.Google Scholar
Valentan, B., Tomaz, B. and Drstvensek, I., (2008), “Basic solutions on shape complexity evaluation of stl data”, Journal of Achievements in Materials and Manufacturing Engineering, 26, 01.Google Scholar
Yang, S., Santoro, F., Sulthan, M.A. and Zhao, Y.F, (2018b), “A numerical-based part consolidation candidate detection approach with modularization considerations”, Research in Engineering Design, Oct. ISSN 1435-6066. http://doi.org/10.1007/s00163-018-0298-3.Google Scholar
Yang, S., Santoro, F. and Zhao, Y.F, (2018a), “Towards a numerical approach of finding candidates for additive manufacturing-enabled part consolidation”, Journal of Mechanical Design, Vol. 140 No. 4:041701–041701-13, Jan. ISSN 1050-0472. http://doi.org/10.1115/1.4038923.Google Scholar
Yang, S. and Zhao, Y.F, (2018), “Additive manufacturing-enabled part count reduction: A lifecycle perspective”, Journal of Mechanical Design, Vol. 140 No. 3:031702–031702-12, Jan. ISSN 1050-0472. http://doi.org/10.1115/1.4038922.Google Scholar
Yao, X., Ki Moon, S. and Bi, G., (2017), “A hybrid machine learning approach for additive manufacturing design feature recommendation”, Rapid Prototyping Journal, Vol. 23 No. 6:983–997. http://doi.org/10.1108/RPJ-03-2016-0041.Google Scholar