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METHOD TO DEFINE MEASUREMENT UNCERTAINTY FOR DESIGN SPACE EXPLORATION IN ADDITIVE MANUFACTURING

Published online by Cambridge University Press:  27 July 2021

Valentine Cazaubon ,
Audrey Abi Akle  and
Xavier Fischer
Valentine Cazaubon*
Affiliation:
Arts et Metiers Institute of Technology, University of Bordeaux, CNRS, Bordeaux INP, INRAE, I2M Bordeaux, F-33400 Talence, FRANCE; Univ. Bordeaux, ESTIA Institute of Technology, F-64210 Bidart, FRANCE
Audrey Abi Akle
Affiliation:
Univ. Bordeaux, ESTIA Institute of Technology, F-64210 Bidart, FRANCE
Xavier Fischer
Affiliation:
Arts et Metiers Institute of Technology, University of Bordeaux, CNRS, Bordeaux INP, INRAE, I2M Bordeaux, F-33400 Talence, FRANCE; Univ. Bordeaux, ESTIA Institute of Technology, F-64210 Bidart, FRANCE
*
Cazaubon, Valentine, ESTIA, ESTIA RECHERCHE, France, v.cazaubon@estia.fr

Abstract

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Additive manufacturing is a process used for quick prototyping in industries. Geometrical defects are observed on printed parts. The aim of the paper is to propose a design method to implement measurements uncertainties into a Design Space for Additive Manufacturing parameters selection. To do so, two tests have been realized. The first test consists in determining the instrument’s uncertainty by measuring a standard length several times by an operator. The second test aim to determine the uncertainty within operators mesurement of geometric outputs (clad’s height, clad’s width, dilution’s height, dilution’s width and contact angle). Based on the results of our tests, uncertainties have been applied in our Design Space populated with 31 real printed clads. The uncertainties display with error bars on scatterplots allow to capitalize the knowledge for his/her exploration of the Design Space for future prints. The given information provides to ease the engineer to select the optimal solution (laser power, tool speed and wire feed speed) for his/her given Additive Manufacturing problematic among candidate points

Keywords

Design for Additive Manufacturing (DfAM)Additive ManufacturingUncertainty
Type
Article
Information
Proceedings of the Design Society , Volume 1: ICED21 , August 2021 , pp. 2067 - 2076
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), 2021. Published by Cambridge University Press

References

Abi Akle, A., Minel, S. and Yannou, B. (2017), “Information visualization for selection in Design by Shopping”, Research in Engineering Design, Vol. 28 No. 1, pp. 99–117. https://doi.org/10.1007/s00163-016-0235-2CrossRefGoogle Scholar
Akle, Abi, Yannou, A., and Minel, B., S. (2019), “Information visualisation for efficient knowledge discovery and informed decision in design by shopping”, Journal of Engineering Design, Vol. 30 No. 6, pp. 227–253. https://doi.org/10.1080/09544828.2019.1623383CrossRefGoogle Scholar
Adamczak, S., Bochnia, J. and Kaczmarska, B. (2014), “Estimating the Uncertainty of Tensile Strength Measurement for A Photocured Material Produced by Additive Manufacturing”, Metrology and Measurement Systems, Vol. 21 No. 3, pp. 553–560. http://doi.org/10.2478/mms-2014-0047CrossRefGoogle Scholar
Bikas, H., Stavropoulos, P. and Chryssolouris, G. (2016), “Additive manufacturing methods and modelling approaches: a critical review”, The International Journal of Advanced Manufacturing Technology, Vol. 83 No. 1-4, pp. 389–405. http://doi.org/10.1007/s00170-015-7576-2CrossRefGoogle Scholar
Cazaubon, V. bi Akle, Fischer, A., X. (2021), “A Parametric Study of Additive Manufacturing Process: TA6V Laser Wire Metal Deposition”, Proceedings of the International Joint Conference on Mechanics, Design Engineering & Advanced Manufacturing (JCM 2020). http://doi.org/10.1007/978-3-030-70566-4_4Google Scholar
Cunningham, C.R., Flynn, J.M., Shokrani, A., Dhokia, V. and Newman, S.T. (2018), “Invited review article: Strategies and processes for high quality wire arc additive manufacturing”, Additive Manufacturing, Vol. 22, pp. 672–686. http://doi.org/10.1016/j.addma.2018.06.020CrossRefGoogle Scholar
Frazier, W.E. (2014), “Metal Additive Manufacturing: A Review”, Journal of Materials Engineering and Performance, Vol. 23 No. 6, pp. 1917–1928. http://doi.org/10.1007/s11665-014-0958-zCrossRefGoogle Scholar
Goguelin, S., Flynn, J.M., Essink, W.P. and Dhokia, V. (2017), “A Data Visualization Dashboard for Exploring the Additive Manufacturing Solution Space”, Procedia CIRP, Vol. 60, pp. 193–198. http://doi.org/10.1016/j.procir.2017.01.016CrossRefGoogle Scholar
Laverne, F., Segonds, F., D'Antonio, G. and Le Coq, M. (2017), “Enriching design with X through tailored additive manufacturing knowledge: a methodological proposal”, International Journal on Interactive Design and Manufacturing (IJIDeM ), Vol. 11 No. 2, pp. 279–288. http://doi.org/10.1007/s12008-016-0314-7Google Scholar
Lopez, F., Witherell, P. and Lane, B. (2016), “Identifying Uncertainty in Laser Powder Bed Fusion Additive Manufacturing Models”, Journal of Mechanical Design, Vol. 138 No. 11, p. 114502. http://doi.org/10.1115/1.4034103CrossRefGoogle Scholar
Lundbäck, A. and Lindgren, L.-E. (2017), “Finite Element Simulation to Support Sustainable Production by Additive Manufacturing”, Procedia Manufacturing, Vol. 7, pp. 127–130. http://doi.org/10.1016/j.promfg.2016.12.033CrossRefGoogle Scholar
Müller, A.M., Oberleiter, T., Willner, K. and Hausotte, T. (2019), “Implementation of Parameterized Work Piece Deviations and Measurement Uncertainties into Performant Meta-models for an Improved Tolerance Specification”, Proceedings of the Design Society: International Conference on Engineering Design, Vol. 1 No. 1, pp. 3501–3510. http://doi.org/10.1017/dsi.2019.357CrossRefGoogle Scholar
Ponche, R., Kerbrat, O., Mognol, P. and Hascoet, J.-Y. (2014), “A novel methodology of design for Additive Manufacturing applied to Additive Laser Manufacturing process”, Robotics and Computer-Integrated Manufacturing, Vol. 30 No. 4, pp. 389–398. http://doi.org/10.1016/j.rcim.2013.12.001CrossRefGoogle Scholar
Ranatowski, E., 2008. “Weldability of Titanium and its Alloys - Progress in Joining”. Advances in Materials Sciences, Vol. 8 No. (2). http://doi.org/10.2478/v10077-008-0033-2Google Scholar
The International Bureau of Weights and Measures. (2008), Evaluation of Measurement Data — Guide to the Expression of Uncertainty in Measurement, available at: https://www.bipm.org/utils/common/documents/jcgm/JCGM_100_2008_E.pdf (accessed 1 December 2020).Google 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/j.cirp.2020.05.006Google Scholar
Watschke, H., Kuschmitz, S., Heubach, J., Lehne, G. and Vietor, T. (2019), “A Methodical Approach to Support Conceptual Design for Multi-Material Additive Manufacturing”, Proceedings of the Design Society: International Conference on Engineering Design, Vol. 1 No. 1, pp. 659–668. http://doi.org/10.1017/dsi.2019.70CrossRefGoogle Scholar
Wu, B., Pan, Z., Ding, D., Cuiuri, D., Li, H., Xu, J. and Norrish, J. (2018), “A review of the wire arc additive manufacturing of metals: properties, defects and quality improvement”, Journal of Manufacturing Processes, Vol. 35, pp. 127–139. http://doi.org/10.1016/j.jmapro.2018.08.001CrossRefGoogle Scholar
Xiong, Y., Duong, P.L.T., Wang, D., Park, S.-I., Ge, Q., Raghavan, N. and Rosen, D.W. (2019), “Data-Driven Design Space Exploration and Exploitation for Design for Additive Manufacturing”, Journal of Mechanical Design, Vol. 141 No. 10, p. 101101. http://doi.org/10.1115/1.4043587CrossRefGoogle Scholar
Yang, S., Tang, Y. & Zhao, Y.F. (2016), “Assembly-Level Design for Additive Manufacturing: Issues and Benchmark”, 42nd Design Automation Conference, Vol. 2A. http://doi.org/10.1115/detc2016-59565.CrossRefGoogle Scholar