Hostname: page-component-586b7cd67f-l7hp2 Total loading time: 0 Render date: 2024-11-24T00:29:23.905Z Has data issue: false hasContentIssue false
  • English
  • Français

Optimization-based design support for engineer-to-order product quotation

Published online by Cambridge University Press:  16 May 2024

Olle Vidner ,
Anton Wiberg ,
Robert Pettersson ,
Johan A. Persson  and
Johan Ölvander
Olle Vidner*
Affiliation:
Linköping University, Sweden
Anton Wiberg
Affiliation:
Linköping University, Sweden
Robert Pettersson
Affiliation:
Epiroc Rock Drills AB, Sweden
Johan A. Persson
Affiliation:
Linköping University, Sweden
Johan Ölvander
Affiliation:
Linköping University, Sweden
*
[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.

Quotation of engineer-to-order products provides substantial challenges in effectively managing engineering resources. This paper describes an approach that rationalizes this process by integrating multi-disciplinary design analysis and optimization with a new open-source library for managing engineering knowledge before and after optimization. The approach is applied and evaluated on mechanical rock excavation machines. Adapting the approach and considering the user feedback gathered can lead to an enhanced design space overview during quotation and thus more competitive product offerings.

Keywords

mass customisationmulti-/cross-/trans-disciplinary approachesdesign optimisationcomputational design methodsknowledge management
Type
Design Methods and Tools
Information
Proceedings of the Design Society , Volume 4: DESIGN 2024 , May 2024 , pp. 825 - 834
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), 2024.

References

Bergling, G., Olofsson, E., Saf, F., Hartwig, S., 2020. Mining machine and method for operating a mining machine. EP3405648B1.Google Scholar
Cederfeldt, M., Elgh, F., 2005. Design automation in SMEs – current state, potential, need and requirements, in: Proceedings of ICED05.Google Scholar
Deb, K., Pratap, A., Agarwal, S., Meyarivan, T., 2002. A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Trans. Evol. Comput. 6, 182–197. https://doi.org/10/bnw2vvCrossRefGoogle Scholar
Elgh, F., 2012. Decision support in the quotation process of engineered-to-order products. Adv. Eng. Inform. 26, 66–79. https://doi.org/10/bbswz2CrossRefGoogle Scholar
Gosling, J., Hewlett, B., Naim, M.M., 2017. Extending customer order penetration concepts to engineering designs. Int. J. Oper. Prod. Manag. 37, 402–422. https://doi.org/10/ghh2kjCrossRefGoogle Scholar
Gray, J.S., Hwang, J.T., Martins, J.R.R.A., Moore, K.T., Naylor, B.A., 2019. OpenMDAO: an open-source framework for multidisciplinary design, analysis, and optimization. Struct. Multidiscip. Optim. 59, 1075–1104. https://doi.org/10/gfwtcbCrossRefGoogle Scholar
Hartwig, S., Delabbio, F., 2010. New tunnel boring machine accelerates tunnel construction in deep mining operations. Min. Eng. 62, 28–34.Google Scholar
Hicks, C., McGovern, T., Earl, C.F., 2000. Supply chain management: a strategic issue in engineer to order manufacturing. Int. J. Prod. Econ. 65, 179–190. https://doi.org/10/cvgc3wCrossRefGoogle Scholar
Hoyer, S., Hamman, J.J., 2017 . xarray: N-D labeled arrays and datasets in Python. J. Open Res. Softw. 5, 10. https://doi.org/10/gdqdmwCrossRefGoogle Scholar
Jin, X., Zhao, G., Zhao, L., Lin, G., 2022. Computer-aided efficient design and performance optimization of cutting head for roadheader. Sci. Rep. 12, 6788. https://doi.org/10/gq2jvfCrossRefGoogle ScholarPubMed
Johansson, J., Elgh, F., 2019. Knowledge objects enable mass-individualization. Springer, Cham, pp. 371–386. https://doi.org/10/gd8t3sCrossRefGoogle Scholar
Konijnendijk, P.A., 1994. Coordinating marketing and manufacturing in ETO companies. Int. J. Prod. Econ. https://doi.org/10/bmghfwGoogle Scholar
Lawrie, D., Morrell, C., Feild, H., Binkley, D., 2007. Effective identifier names for comprehension and memory. Innov. Syst. Softw. Eng. 3, 303–318. https://doi.org/10/dbvkxkCrossRefGoogle Scholar
Lislerud, A., 1997. Principles of mechanical excavation (POSIVA 97-12). Posiva, Helsinki, Finland.Google Scholar
Maidl, B., Wehrmeyer, G., Derbort, M. (Eds.), 2008. Hardrock tunnel boring machines. Ernst & Sohn, Berlin.CrossRefGoogle Scholar
Melbro, A., Jersenius, E., 2018. Design optimization of a Mobile Miner [M.Sc.Eng. Thesis]. Linköping University.Google Scholar
Modelica Association, 2022. Functional mock-up interface specification (Standard).Google Scholar
Muller, G.J., Heemels, W.P.M.H., 2007. Five years of multi-disciplinary academic and industrial research: lessons learned, in: Proceedings of CSER2007. Hoboken, New Jersey.Google Scholar
Object Management Group, 2017. Unified Modeling Language specification.Google Scholar
Papageorgiou, A., Ölvander, J., 2017. The role of multidisciplinary design optimization (MDO) in the development process of complex engineering products. Proceedings of ICED 17.Google Scholar
Potts, C., 1993. Software-engineering research revisited. IEEE Software 10. https://doi.org/10/dkgbtjCrossRefGoogle Scholar
Rostami, J., Ozdemir, L., Neil, D.M., 1994. Roadheaders performance optimization for mining and civil construction, in: Proceedings of ISDT. Las Vegas.Google Scholar
Sargent, R., 2013. Verification and validation of simulation models. J. Simul. 13. https://doi.org/10/gfgzcxGoogle Scholar
Siemens PLM, 2018. HEEDS MDO.Google Scholar
Simpson, T.W., Martins, J.R.R.A., 2011. Multidisciplinary design optimization for complex engineered systems. J. Mech. Des. 133, 101002–1--101002–10. https://doi.org/10/b2s9brCrossRefGoogle Scholar
Skawina, B., Greberg, J., Salama, A., Schunnesson, H., 2014. Mechanical excavation and drilling and blasting – a comparison using discrete event simulation, in: Mine Planning and Equipment Selection. Springer, Cham, pp. 367–377. https://doi.org/10/f2z2m2Google Scholar
Sobieszczanski-Sobieski, J., Morris, A., van Tooren, M.J.L., 2015. Multidisciplinary design optimization supported by knowledge based engineering. Wiley, Chichester, UK.CrossRefGoogle Scholar
Sugden, D.B., Boyd, R.J., 1988. Development of the Robbins Mobile Miner. Tunn. Undergr. Space Technol. 3, 153–157. https://doi.org/10/dq39ptCrossRefGoogle Scholar
Sun, W., Wang, X., Shi, M., Wang, Z., Song, X., 2018. Multidisciplinary design optimization of hard rock tunnel boring machine using collaborative optimization. Adv. Mech. Eng. 10. https://doi.org/10/gjsffvGoogle Scholar
Ulrich, K.T., 2003. Product design and development. McGraw-Hill.Google Scholar
van Gent, I., Aigner, B., Beijer, B., Jepsen, J., La Rocca, G., 2020. Knowledge architecture supporting the next generation of MDO in the AGILE paradigm. Prog. Aerosp. Sci. 119. https://doi.org/10/gq3sh4CrossRefGoogle Scholar
van Gent, I., La Rocca, G., Hoogreef, M.F.M., 2018. CMDOWS: a proposed new standard to store and exchange MDO systems. CEAS Aeronaut. J. 9, 607–627. https://doi.org/10/gh25s4CrossRefGoogle Scholar
Verhagen, W.J.C., Bermell-Garcia, P., van Dijk, R.E.C., Curran, R., 2012. A critical review of knowledge-based engineering. Adv. Eng. Inf. https://doi.org/10/dgk67xGoogle Scholar
Vidner, O., 2023a. Facit. https://doi.org/10.5281/ZENODO.7574041CrossRefGoogle Scholar
Vidner, O., 2023b. OpenMDAO-NSGA. https://doi.org/10.5281/zenodo.4279483CrossRefGoogle Scholar
Vidner, O., Pettersson, R., Persson, J.A., Ölvander, J., 2021a. Multidisciplinary design optimization of a Mobile Miner using the OpenMDAO platform. Proceedings of ICED 21. https://doi.org/10/grrpGoogle Scholar
Vidner, O., Wehlin, C., Persson, J.A., Ölvander, J., 2021b. Configuring customized products with design optimization and value-driven design. Proceedings of ICED 21. https://doi.org/10/grrmGoogle Scholar
Vogt, D., 2016. A review of rock cutting for underground mining: past, present, and future. J. South. Afr. Inst. Min. Metall. 116, 1011–1026. https://doi.org/10/f939nrCrossRefGoogle Scholar
Wang, L., Sun, W., Long, Y., Yang, X., 2018. Reliability-based performance optimization of tunnel boring machine considering geological uncertainties. IEEE Access 6. https://doi.org/10/gq2jv5Google Scholar
Wehlin, C., 2021. Optimization-based configurators in the product development process [Licentiate Thesis]. Linköping University. https://doi.org/10/gkmw79CrossRefGoogle Scholar