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FUNCTION DRIVEN ASSESSMENT OF MANUFACTURING RISKS IN CONCEPT GENERATION STAGES
Published online by Cambridge University Press: 19 June 2023
Abstract
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Decisions made in the concept generation phase have a significant effect on the product. While product- related risks typically can be considered in the early stages of design, risks such as supply chain and manufacturing methods are rarely easy to account for in early phases. This is because the currently available methods require mature data, which may not be available during concept generation. In this paper, we propose an approach to address this. First, the product and the non-product (manufacturing and/or supply chain) attributes are modelled using the enhanced function means (EF-M) modelling method. The EF-M method provides the opportunity to model alternative solutions-set for functions. Dependencies are then mapped within the product and the manufacturing models, and also in between them. An automatic combinatorial method of concept generation is employed where each generated instance is a design concept-manufacturing method pair. A risk propagation algorithm is then used to assess the risks of all the generated alternatives.
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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
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References
ACARE (2020), Time for change - The need to rethink Europe's FlightPath 2050, Technical report, Advisory Council for Aviation Research and Innovation in Europe.Google Scholar
Aguila, J.O. and ElMaraghy, W. (2018), “Structural complexity and robustness of supply chain networks based on product architecture”, IntJProdRes, Vol. 56 No. 20, pp. 6701–6718, http://doi.org/10.1080/00207543.2018.1489158.Google Scholar
Brahma, A. and Wynn, D.C. (2022), “Concepts of change propagation analysis in engineering design”, Res Eng Des, pp. 1–35, http://doi.org/10.1007/s00163-022-00395-y.CrossRefGoogle Scholar
Brouckaert, J.F., Mirville, F., Phuah, K. and Taferner, P. (2018), “Clean sky research and demonstration programmes for next-generation aircraft engines”, The Aeronautical Journal, Vol. 122 No. 1254, p. 1163–1175, http://doi.org/10.1017/aer.2018.37.CrossRefGoogle Scholar
Clarkson, P.J., Simons, C. and Eckert, C. (2004), “Predicting change propagation in complex design”, J. Mech. Des., Vol. 126 No. 5, pp. 788–797, http://doi.org/10.1115/L1765117.CrossRefGoogle Scholar
De Weck, O., Eckert, C.M., Clarkson, P.J. et al. (2007), “A classification of uncertainty for early product and system design”, in: DS 42: Proceedings of ICED 2007, the 16th International Conference on Engineering Design, Paris, France, 28.-31.07. 2007, pp. 159–160.Google Scholar
Earl, C., Johnson, J. and Eckert, C. (2005), “Complexity”, in: Clarkson, J. and Eckert, C. (Editors), Design process improvement: A review of current practice, Springer, UK, pp. 174–197, http://doi.org/10.1007/978-1-84628-061-0_8.CrossRefGoogle Scholar
Eisenbart, B., Gericke, K., Blessing, L.T. and McAloone, T.C. (2017), “A dsm-based framework for integrated function modelling: concept, application and evaluation”, Res Eng Des, Vol. 28 No. 1, pp. 25–51, http://doi.org/10.1007/s00163-016-0228-1.CrossRefGoogle Scholar
Gilchrist, W. (1993), “Modelling failure modes and effects analysis”, International Journal of Quality & Reliability Management, Vol. 10 No. 5, http://doi.org/10.1108/02656719310040105.CrossRefGoogle Scholar
Grebici, K., Goh, Y. and McMahon, C. (2008), “Uncertainty and risk reduction in engineering design embodiment processes”, in: DS 48: Proceedings DESIGN 2008, the 10th International Design Conference, Dubrovnik, Croatia, pp. 143–156.Google Scholar
Hamraz, B., Hisarciklilar, O., Rahmani, K., Wynn, D.C., Thomson, V. and Clarkson, P.J. (2013), “Change prediction using interface data”, Concurrent Eng.: Res. Appl., Vol. 21 No. 2, pp. 141–154, http://doi.org/10.1177/1063293X13482473.CrossRefGoogle Scholar
Henderson, R.M. and Clark, K.B. (1990), “Architectural innovation: The reconfiguration of existing product technologies and the failure of established firms”, Admin Sci Quart, pp. 9–30, http://doi.org/10.2307/2393549.CrossRefGoogle Scholar
Isaksson, O., Kipouros, T., Martinsson, J., Panarotto, M., Kressin, J., Andersson, P. and Clarkson, J.P. (2021), “Multi-Domain Design Assessment for Aerospace Components Including Weld Accessibility”, in: Proceedings of the International Conference on Engineering Design, Vol. 1, pp. 2217–2226, http://doi.org/10.1017/pds.2021.483.Google Scholar
Kaplan, S. and Garrick, B.J. (1981), “On the quantitative definition of risk”, Risk Analysis, Vol. 1 No. 1, pp. 11–27, http://doi.org/10.1111/j.1539-6924.1981.tb01350.x.CrossRefGoogle Scholar
Landahl, J., Jiao, R.J., Madrid, J., Soderberg, R. and Johannesson, H. (2021), “Dynamic platform modeling for concurrent product-production reconfiguration”, Concurrent Eng.: Res. Appl., Vol. 29 No. 2, pp. 102–123, http://doi.org/10.1177/1063293X20958938.CrossRefGoogle Scholar
Lough, K.G., Stone, R. and Tumer, I.Y. (2009), “The risk in early design method”, J Eng Des, Vol. 20 No. 2, pp. 155–173, http://doi.org/10.1080/09544820701684271.CrossRefGoogle Scholar
Mobey, A. and Parker, D. (2002), “Risk evaluation and its importance to project implementation”, Work Study, Vol. 51 No. 4, pp. 202–208, http://doi.org/10.1108/00438020210430760.CrossRefGoogle Scholar
Muller, J.R., Isaksson, O., Landahl, J., Raja, V., Panarotto, M., Levandowski, C. and Raudberget, D. (2019), “Enhanced function-means modeling supporting design space exploration”, AI EDAM, Vol. 33 No. 4, pp. 502–516, http://doi.org/10.1017/S0890060419000271.Google Scholar
Panarotto, M., Kipouros, T., Brahma, A., Isaksson, O., Strandh Tholin, O., Clarkson, J. et al. (2022), “Using dsms in functionally driven explorative design experiments-an automation approach”, in: DS121: Proceedings of the 24th International DSM Conference (DSM 2022), Eindhoven, The Netherlands, October, 11-13, 2022, pp. 68–77, http://doi.org/10.35199/dsm2022.08.CrossRefGoogle Scholar
Patterson, A.E., Chadha, C. and Jasiuk, I.M. (2021), “Identification and mapping of manufacturability constraints for extrusion-based additive manufacturing”, Journal of Manufacturing and Materials Processing, Vol. 5 No. 2, p. 33, http://doi.org/10.3390/jmmp5020033.CrossRefGoogle Scholar
Raja, V., Kokkolaras, M. and Isaksson, O. (2019), “A simulation-assisted complexity metric for design optimization of integrated architecture aero-engine structures”, Struct Multidiscipl Optim, Vol. 60 No. 1, pp. 287–300, http://doi.org/10.1007/s00158-019-02308-5.CrossRefGoogle Scholar
Raudberget, D., Levandowski, C., Isaksson, O., Kipouros, T., Johannesson, H. and Clarkson, J. (2015), “Modelling and assessing platform architectures in pre-embodiment phases through set-based evaluation and change propagation”, Journal of Aerospace Operations, Vol. 3 No. 3-4, pp. 203–221.CrossRefGoogle Scholar
Seebode, D., Jeanrenaud, S. and Bessant, J. (2012), “Managing innovation for sustainability”, R&D Manage, Vol. 42 No. 3, pp. 195–206, http://doi.org/10.1111/jM467-9310.2012.00678.x.Google Scholar
Sobek, D.K. II, Ward, A.C. and Liker, J.K. (1999), “Toyota's principles of set-based concurrent engineering”, MIT Sloan Management Review, Vol. 40 No. 2, p. 67.Google Scholar
Stamatelatos, M., Dezfuli, H., Apostolakis, G., Everline, C., Guarro, S., Mathias, D., Mosleh, A., Paulos, T., Riha, D., Smith, C. et al. (2011), Probabilistic risk assessment procedures guide for NASA managers and practitioners, Technical report, NASA.Google Scholar
Tan, J.J.Y., Otto, K.N. and Wood, K.L. (2017), “Relative impact of early versus late design decisions in systems development”, Design Science, Vol. 3, p. e12, http://doi.org/10.1017/dsj.2017.13.CrossRefGoogle Scholar
Turrin, M., Von Buelow, P. and Stouffs, R. (2011), “Design explorations of performance driven geometry in architectural design using parametric modeling and genetic algorithms”, Adv Eng Inform, Vol. 25 No. 4, pp. 656–675, http://doi.org/10.1016/jj.aei.2011.07.009.CrossRefGoogle Scholar
Ulrich, K. (1995), “The role of product architecture in the manufacturing firm”, Res Policy, Vol. 24 No. 3, pp. 419–440, http://doi.org/10.1016/0048-7333(94)00775-3.CrossRefGoogle Scholar
Ulrich, K. and Eppinger, S. (2012), Product Design and Development, McGraw-Hill/Irwin.Google Scholar
Vesely, W.E., Goldberg, F.F., Roberts, N.H. and Haasl, D.F. (1981), Fault tree handbook, Technical report, Nuclear Regulatory Commission Washington DC.Google Scholar
Wynn, D., Wyatt, D.F., Nair, S. and Clarkson, J. (2010), “An introduction to the Cambridge Advanced Modeller”, in: Proceedings of the 1st International Conference on Modelling and Management of Engineering Processes (MMEP 2010). Cambridge, UK, 19-20 July 2010, pp. 1–4.Google Scholar
Wynn, D.C., Caldwell, N.H. and John Clarkson, P. (2014), “Predicting change propagation in complex design workflows”, J. Mech. Des., Vol. 136 No. 8, http://doi.org/10.1115/L4027495.CrossRefGoogle Scholar
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