Hostname: page-component-cd9895bd7-jn8rn Total loading time: 0 Render date: 2024-12-26T08:41:55.780Z Has data issue: false hasContentIssue false
  • English
  • Français

Challenges in the industrial implementation of generative design systems: An exploratory study

Published online by Cambridge University Press:  30 January 2017

Axel Nordin
Axel Nordin*
Affiliation:
Division of Product Development, Department of Design Sciences, Faculty of Engineering LTH, Lund University, Lund, Sweden
*
Reprint requests to: Axel Nordin, Division of Product Development, Department of Design Sciences, Faculty of Engineering LTH, Lund University, P.O. Box 118, Lund 221 00, Sweden. E-mail: [email protected]
Get access Rights & Permissions [Opens in a new window]

Abstract

The aim of this paper is to investigate the challenges associated with the industrial implementation of generative design systems. Though many studies have been aimed at validating either the technical feasibility or the usefulness of generative design systems, there is, however, a lack of research on the practical implementation and adaptation in industry. To that end, this paper presents two case studies conducted while developing design systems for industrial uses. The first case study focuses on an engineering design application and the other on an industrial design application. In both cases, the focus is on detail-oriented performance-driven generative design systems based on currently available computer-assisted design tools. The development time and communications with the companies were analyzed to identify challenges in the two projects. Overall, the results show that the challenges are not related to whether the design tools are intended for artistic or technical problems, but rather in how to make the design process systematic. The challenges include aspects such as how to fully utilize the potential of generative design tools in a traditional product development process, how to enable designers not familiar with programming to provide design generation logic, and what should be automated and what is better left as a manual task. The paper suggests several strategies for dealing with the identified challenges.

Keywords

Case StudyDesign AutomationEngineering DesignGenerative DesignIndustrial Design
Type
Regular Articles
Information
AI EDAM , Volume 32 , Issue 1 , February 2018 , pp. 16 - 31
Copyright
Copyright © Cambridge University Press 2017 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

REFERENCES

Agarwal, M., & Cagan, J. (1998). A blend of different tastes: the language of coffee makers. Environment and Planning B 25 (2), 205–227.CrossRefGoogle Scholar
Agarwal, M., Cagan, J., & Constantine, C.G. (1999). Influencing generative design through continuous evaluation: associating costs with the coffeemaker shape grammar. Artificial Intelligence for Engineering Design, Analysis and Manufacturing 13 (4), 253–275.CrossRefGoogle Scholar
Ahlquist, S., Erb, D., & Menges, A. (2015). Evolutionary structural and spatial adaptation of topologically differentiated tensile systems in architectural design. Artificial Intelligence for Engineering Design, Analysis and Manufacturing 29 (04), 393–415.Google Scholar
Bentley, P.J., & Corne, D.W. (2002). Creative Evolutionary Systems (Bentley, P.J., & Corne, D.W., Eds.). San Francisco, CA: Morgan Kaufmann.Google Scholar
Blessing, L.T.M., & Chakrabarti, A. (2009). DRM: A Design Research Methodology. London: Springer.Google Scholar
Cagan, J., Campbell, M.I., Finger, S., & Tomiyama, T. (2005). A framework for computational design synthesis: model and applications. Journal of Computing and Information Science in Engineering 5 (3), 171–181.Google Scholar
Chau, H.H., Chen, X., McKay, A., & de Pennington, A. (2004). Evaluation of a 3D shape grammar implementation. Proc. 1st Design Computing and Cognition Conf., DCC'04 (Gero, J.S., Ed.), pp. 357–376. Dordrecht: Kluwer.Google Scholar
Cluzel, F., Yannou, B., & Dihlmann, M. (2012). Using evolutionary design to interactively sketch car silhouettes and stimulate designer's creativity. Engineering Applications of Artificial Intelligence 25 (7), 1413–1424.CrossRefGoogle Scholar
Coumans, E. (2015). Bullet Physics Library. Accessed August 6, 2015, at http://bulletphysics.org/ Google Scholar
Eisenhardt, K.M. (1989). Building theories from case study research. Academy of Management Review 14 (4), 532–550.Google Scholar
Eriksson, M. (2015). Fundamentals of a methodology for predictive design analysis. Unpublished manuscript, Lund University.Google Scholar
Esteco. (2015). modeFRONTIER 2014. Accessed at http://www.esteco.com/modefrontier Google Scholar
Frazer, J. (2002). Creative design and the generative evolutionary paradigm. In Creative Evolutionary Systems (Bentley, P.J., & Corne, D.W., Eds.), pp. 253–274. San Francisco, CA: Morgan Kaufmann.Google Scholar
Freelon, D.G. (2010). ReCal: intercoder reliability calculation as a web service. International Journal of Internet Science 5 (1), 20–33.Google Scholar
Glaser, B.G., & Strauss, A.L. (1967). The Discovery of Grounded Theory: Strategies for Qualitative Research. Chicago: Aldine.Google Scholar
Goodman, E., Stolterman, E., & Wakkary, R. (2011). Understanding interaction design practices. Proc. 2011 Annual Conf. Human Factors in Computing Systems, CHI ’11, pp. 1061–1070, Vancouver.Google Scholar
Holland, J.H. (1975). Adaptation in Natural and Artificial Systems. Ann Arbor, MI: University of Michigan Press.Google Scholar
Hornby, G.S., Lipson, H., & Pollack, J.B. (2001). Evolution of generative design systems for modular physical robots. Proc. IEEE Int. Conf. Robotics and Automation, Vol. 4, pp. 4146–4151, Seoul, Korea.Google Scholar
Horváth, I. (2005). On some crucial issues of computer support of conceptual design. In Product Engineering: Eco-Design, Technologies and Green Energy (Talabă, D., & Roche, T., Eds.), pp. 123–142. Dordrecht: Springer.CrossRefGoogle Scholar
Hsieh, H.-F., & Shannon, S.E. (2005). Three approaches to qualitative content analysis. Qualitative Health Research 15 (9), 1277–1288.Google Scholar
Janssen, P. (2006). A generative evolutionary design method. Digital Creativity 17 (1), 49–63.Google Scholar
Janssen, P. (2015). Dexen: a scalable and extensible platform for experimenting with population-based design exploration algorithms. Artificial Intelligence for Engineering Design, Analysis and Manufacturing 29 (4), 443–455.Google Scholar
Knight, T.W. (1980). The generation of Hepplewhite-style chair-back designs. Environment and Planning B 7 (2), 227–238.Google Scholar
Knuth, D.E. (1995). The METAFONT Book, 2nd ed. Reading, MA: Addison-Wesley.Google Scholar
Krippendorff, K. (1970). Bivariate agreement coefficients for reliability of data. Sociological Methodology 2, 139–150.Google Scholar
Krippendorff, K. (2004). Reliability in content analysis: some common misconceptions and recommendations. Human Communication Research 30 (3), 411–433.Google Scholar
Krish, S. (2011). A practical generative design method. Computer-Aided Design 43 (1), 88–100.Google Scholar
Lindenmayer, A. (1968). Mathematical models for cellular interactions in development, parts I and II. Journal of Theoretical Biology 18, 280–315.Google Scholar
Lombard, M., Snyder-Duch, J., & Campanella Bracken, C. (2002). Content analysis in mass communication. Human Communication Research 28 (4), 587–604.Google Scholar
McCormack, J.P., Cagan, J., & Vogel, C.M. (2004). Speaking the Buick language: capturing, understanding, and exploring brand identity with shape grammars. Design Studies 25 (1), 1–29.Google Scholar
Merriam, S.B. (1998). Qualitative Research and Case Study Applications in Education, 2nd ed. San Francisco, CA: Jossey-Bass.Google Scholar
Nordin, A., Hopf, A., Motte, D., Bjärnemo, R., & Eckhardt, C.-C. (2011). An approach to constraint-based and mass-customizable product design. Journal of Computing and Information Science in Engineering 11 (1), 011006–011012.Google Scholar
Nordin, A., Motte, D., & Bjärnemo, R. (2013). Strategies for consumer control of complex product forms in generative design systems. Proc. 39th Design Automation Conf., DETC/DAC'13, pp. 1–10. Portland, OR: ASME.Google Scholar
Nordin, A., Motte, D., Hopf, A., Bjärnemo, R., & Eckhardt, C.-C. (2010). Complex product form generation in industrial design: a bookshelf based on Voronoi diagrams. Proc. 4th Design Computing and Cognition Conf., DCC'10 (Gero, J.S., Ed.), pp. 701–720. Dordrecht: Springer.Google Scholar
Orsborn, S., Cagan, J., Pawlicki, R., & Smith, R.C. (2006). Creating cross-over vehicles: defining and combining vehicle classes using shape grammars. Artificial Intelligence for Engineering Design, Analysis and Manufacturing 20 (3), 217–246.CrossRefGoogle Scholar
Oxman, R. (2006). Theory and design in the first digital age. Design Studies 27 (3), 229–265.CrossRefGoogle Scholar
Pahl, G., Beitz, W., Feldhusen, J., & Grote, K.-H. (2007). In Engineering Design—A Systematic Approach (Wallace, K.M., & Blessing, L.T.M., Eds.), 3rd ed. London: Springer.Google Scholar
Poles, S., Rigoni, E., & Robič, T. (2004). MOGA-II performance on noisy optimization problems. Proc. Int. Conf. Bioinspired Optimization Methods and Their Applications, pp. 51–62. Ljubljana: Jozef Stefan Institute.Google Scholar
PTC. (2013). Creo 2.0 [Computer software]. Accessed at http://www.ptc.com/cad/3d-cad/creo-parametric Google Scholar
Pugliese, M.J., & Cagan, J. (2002). Capturing a rebel: modeling the Harley-Davidson brand through a motorcycle shape grammar. Research in Engineering Design 13 (3), 139–156.Google Scholar
Robert McNeel & Associates. (2014). Grasshopper [Computer software]. Accessed at http://www.grasshopper3d.com/ Google Scholar
Robert McNeel & Associates. (2015). Rhinoceros 3D [Computer software]. Accessed at http://www.rhino3d.com/ Google Scholar
Roedl, D., & Stolterman, E. (2013). Design research at CHI and its applicability to design practice. Proc. SIGCHI Conf. Human Factors in Computing Systems, pp. 1951–1954. New York: ACM.Google Scholar
Shea, K., Aish, R., & Gourtovaia, M. (2005). Towards integrated performance-driven generative design tools. Automation in Construction 14 (2), 253–264.Google Scholar
Simon, H.A. (1973). The structure of ill structured problems. Artificial Intelligence 4 (3–4), 181–201.CrossRefGoogle Scholar
Sims, K. (1991). Artificial evolution for computer graphics. Proc. 18th Annual Conf. Computer Graphics and Interactive Techniques, pp. 319–328. New York: ACM.Google Scholar
Singh, V., & Gu, N. (2012). Towards an integrated generative design framework. Design Studies 33 (2), 185–207.CrossRefGoogle Scholar
Stiny, G. (1980). Introduction to shape and shape grammars. Environment and Planning B 7 (3), 343–351.Google Scholar
Thompson, A. (1996). An evolved circuit, intrinsic in silicon, entwined with physics. Proc. 1st Int. Conf. Evolvable Systems: From Biology to Hardware, pp. 390–405. London: Springer.Google Scholar
Turrin, M., Von Buelow, P., & Stouffs, R. (2011). Design explorations of performance driven geometry in architectural design using parametric modeling and genetic algorithms. Advanced Engineering Informatics 25 (4), 656–675.Google Scholar
Ullman, D.G. (1997). The Mechanical Design Process, 2nd ed. New York: McGraw–Hill.Google Scholar
Ulrich, K.T., & Eppinger, S. (2012). Product Design and Development, 5th ed. New York: McGraw-Hill.Google Scholar
Wolfram, S. (2002). A New Kind of Science. New York: Wolfram media.Google Scholar
Zboinska, M.A. (2014). Hybrid CAD/E platform supporting exploratory architectural design. Computer Aided Design 59, 64–84.Google Scholar