Hostname: page-component-cd9895bd7-p9bg8 Total loading time: 0 Render date: 2024-12-26T15:23:58.888Z Has data issue: false hasContentIssue false

A Knowledge Based Approach to Support the Conceptual Design of ETO Products

Published online by Cambridge University Press:  26 July 2019

Paolo Cicconi
Affiliation:
Università Politecnica delle Marche
Andrea Savoretti
Affiliation:
Università Politecnica delle Marche
Roberto Raffaeli
Affiliation:
Università degli studi eCampus
Michele Germani
Affiliation:
Università Politecnica delle Marche

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.

The ever-increasing competitiveness, due to the market globalization, has forced the industries to modify their design and production strategies. A key point is the development of products that fulfil the individual customer needs as close as possible. ETO companies manufacture new products according to the customer technical requirements given in the request for proposal.

Computational Design Synthesis is the research area focused on activities to automate the design phase in the production of products such ETO structures. In this context, Knowledge Based Engineering applications are usually applied to automate design routines and to implement a multidisciplinary product design. Knowledge should be elicited and formalized, so that it can allow the past cases retrieval and the connection between customer specifications and the product configuration tasks. This paper proposes an approach for the rapid definition of the product structure related to a ETO product, including the early cost evaluation in configurations. The research scope aims at defining a framework to support the knowledge repository, which is the Knowledge Based used to design new products and estimate their costs.

Type
Article
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

Anon (2007), “Factors that influence Manufacturing Costs and Procedures for Cost Reduction”, Cost-Efficient Design, pp. 143384. Available at: http://doi.org/10.1007/978-3-540-34648-7_7Google Scholar
Anon (2016), “Artificial Intelligence Techniques”. Advanced Solutions in Power Systems: HVDC, FACTS, and Artificial Intelligence, pp. 719720. Available at: http://doi.org/10.1002/9781119175391.part3Google Scholar
Cicconi, P., et al. (2018), “A model-based simulation approach to support the product configuration and optimization of gas turbine ducts”, Computer-Aided Design and Applications, Vol. 15 No. 6, pp. 807818. Available at: http://doi.org/10.1080/16864360.2018.1462564.Google Scholar
Hölttä-Otto, K. and Otto, K. (2006), “Platform Concept Evaluation”, Product Platform and Product Family Design, pp. 4972. Available at: http://doi.org/10.1007/0-387-29197-0_4Google Scholar
Kristianto, Y., Helo, P. and Jiao, R.J. (2015), “A system level product configurator for engineer-to-order supply chains”, Computers in Industry, Vol. 72, pp. 8291. Available at: http://doi.org/10.1016/j.compind.2015.04.004Google Scholar
La Rocca, G. (2012), “Knowledge based engineering: Between AI and CAD. Review of a language based technology to support engineering design”, Advanced Engineering Informatics, Vol. 26 No. 2, pp. 159179. Available at: http://doi.org/10.1016/j.aei.2012.02.002.Google Scholar
Lewis, G. (2015), “Product Design for Manufacturing and Assembly. Mechanical Engineers”, Handbook, pp. 119. Available at: http://doi.org/10.1002/9781118985960.meh202.Google Scholar
McMahon, C., Lowe, A. & Culley, S., 2004. Knowledge management in engineering design: personalization and codification”, Journal of Engineering Design, Vol. 15 No. 4, pp. 307325. Available at: http://doi.org/10.1080/09544820410001697154.Google Scholar
Münzer, C. H. (2014). “Constraint-Based Methods for Automated Computational Design Synthesis of Solution Spaces”, Ph.D. Thesis, Technische Universität München, Zurich.Google Scholar
Opiyo, E.Z. (2017), “A feature-based approach to conceptualisation, upfront modelling, and planning for the future of complex systems”, International Journal of Information Technology and Management, Vol. 16 No. 1, p. 91. Available at: http://doi.org/10.1504/ijitm.2017.10001055.Google Scholar
Otto, K., et al. (2016), “Global Views on Modular Design Research: Linking Alternative Methods to Support Modular Product Family Concept Development”, Journal of Mechanical Design, Vol. 138 No. 7, p. 071101. Available at: http://doi.org/10.1115/1.4033654.Google Scholar
Pahl, G., et al. (2007), “Engineering Design”. Available at: http://doi.org/10.1007/978-1-84628-319-2Google Scholar
Raffaeli, R., Mengoni, M. and Germani, M. (2011), “An Early-Stage Tool to Evaluate the Product Redesign Impact”, Vol 5: 37th Design Automation Conference, Parts A and B. Available at: http://doi.org/10.1115/detc2011-47625.Google Scholar
Raffaeli, R., Mengoni, M. & Germani, M. (2013), “Improving the link between computer-assisted design and configuration tools for the design of mechanical products”, Artificial Intelligence for Engineering Design, Analysis and Manufacturing, Vol. 27 No. 01, pp. 5164. Available at: http://doi.org/10.1017/s0890060412000388.Google Scholar
Ripperda, S. and Krause, D. (2017), “Cost Effects of Modular Product Family Structures: Methods and Quantification of Impacts to Support Decision Making”, Journal of Mechanical Design, Vol. 139 No. 2, p. 021103. Available at: http://doi.org/10.1115/1.4035430.Google Scholar
Simpson, T.W. (2017), “Product Family and Product Platform Benchmarking With Commonality and Variety Indices”, Vol. B: 43rd Design Automation Conference. Available at: http://doi.org/10.1115/detc2017-67500.Google Scholar
Tang, D., Yin, L. and Ullah, I. (2017), “Product Design as Integration of Axiomatic Design and Design Structure Matrix”, Matrix-based Product Design and Change Management, pp. 120. Available at: http://doi.org/10.1007/978-981-10-5077-0_1Google Scholar
Thebeau, R. E. (2001), “Knowledge management of system interface and interactions for product development processes”, PhD Thesis, Massachusetts Institute of Technology.Google Scholar
Verhagen, W.J.C., et al. (2012), “A critical review of Knowledge-Based Engineering: An identification of research challenges”, Advanced Engineering Informatics, Vol. 26 No. 1, pp. 515. Available at: http://doi.org/10.1016/j.aei.2011.06.004.Google Scholar
Wang, L., et al. (2002), “Collaborative conceptual design—state of the art and future trends”, Computer-Aided Design, Vol. 34 No. 13, pp. 981996. Available at: http://doi.org/10.1016/s0010-4485(01)00157-9Google Scholar
Windheim, M., Greve, E. and Krause, D. (2017), “Decisive economies and opportunity cost of modular product structure alternatives: An empirical case study”, 2017 IEEE International Conference on Industrial Engineering and Engineering Management (IEEM). Available at: http://doi.org/10.1109/ieem.2017.8290170Google Scholar
Zhu, Z., La Rocca, G. and van Tooren, M.J.L. (2017), “A methodology to enable automatic 3D routing of aircraft Electrical Wiring Interconnection System”, CEAS Aeronautical Journal, Vol. 8 No. 2, pp. 287302. Available at: http://doi.org/10.1007/s13272-017-0238-3.Google Scholar