Hostname: page-component-cd9895bd7-q99xh Total loading time: 0 Render date: 2024-12-25T20:18:00.512Z Has data issue: false hasContentIssue false
  • English
  • Français

Enabling parametric design space exploration by non-designers

Published online by Cambridge University Press:  16 April 2020

Eduardo Castro e Costa Open the ORCID record for Eduardo Castro e Costa [Opens in a new window] ,
Joaquim Jorge ,
Aaron D. Knochel  and
José Pinto Duarte Open the ORCID record for José Pinto Duarte [Opens in a new window]
Eduardo Castro e Costa*
Affiliation:
Stuckeman School of Architecture and Landscape Architecture, The Pennsylvania State University, University Park, State College, PA, USA
Joaquim Jorge
Affiliation:
Department of Computer Science, University of Lisbon, Lisbon, Portugal
Aaron D. Knochel
Affiliation:
School of Visual Arts, The Pennsylvania State University, University Park, State College, PA, USA
José Pinto Duarte
Affiliation:
Stuckeman School of Architecture and Landscape Architecture, The Pennsylvania State University, University Park, State College, PA, USA
*
Author for correspondence: Eduardo Castro e Costa, E-mail: [email protected]
Get access Rights & Permissions [Opens in a new window]

Abstract

In mass customization, software configurators enable novice end-users to design customized products and services according to their needs and preferences. However, traditional configurators hardly provide an engaging experience while avoiding the burden of choice. We propose a Design Participation Model to facilitate navigating the design space, based on two modules. Modeler enables designers to create customizable designs as parametric models, and Navigator subsequently permits novice end-users to explore these designs. While most parametric designs support direct manipulation of low-level features, we propose interpolation features to give customers more flexibility. In this paper, we focus on the implementation of such interpolation features into Navigator and its user interface. To assess our approach, we designed and performed user experiments to test and compare Modeler and Navigator, thus providing insights for further developments of our approach. Our results suggest that barycentric interpolation between qualitative parameters provides a more easily understandable interface that empowers novice customers to explore the design space expeditiously.

Keywords

Barycentric interpolationdesign space explorationmass customizationparametric modelinguser testing
Type
Research Article
Information
AI EDAM , Volume 34 , Special Issue 2: Thematic Collection: Design Cognition and Computing , May 2020 , pp. 160 - 175
Copyright
Copyright © Cambridge University Press 2020

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

Abras, C, Maloney-krichmar, D and Preece, J (2004) User-centered design. In Bainbridge, W (ed), Encyclopedia of Human-Computer Interaction, Vol. 37. Thousand Oaks, CA: Sage Publications, pp. 445–456.Google Scholar
Berger, C and Piller, F (2003) Customers as co-designers. Manufacturing Engineer 82, 42–45.CrossRefGoogle Scholar
Bjögvinsson, E, Ehn, P and Hillgren, P-A (2012) Design things and design thinking: contemporary participatory design challenges. Design Issues 28, 101–116.CrossRefGoogle Scholar
Blazek, P, Partl, M and Streichsbier, C (2017) Configurator Database Report 2016. Morrisville, North Carolina: lulu.com.Google Scholar
Blyth, TS and Robertson, EF (2013) Basic Linear Algebra. Berlin/Heidelberg, Germany: Springer Science & Business Media.Google Scholar
Bodker, S (1996) Creating conditions for participation: conflicts and resources in systems development. Human–Computer Interaction 11, 215–236.10.1207/s15327051hci1103_2CrossRefGoogle Scholar
Capterra (2019) ShapeDiver Reviews and Pricing – 2019. Available at https://www.capterra.com/p/165993/ShapeDiver/ (Retrieved 28 August 2019).Google Scholar
Castro e Costa, E (2018) Managing Complexity in Mass Customization through Design Space Subdivision: A Case Study in Ceramic Tableware Design (PhD in architecture). Pennsylvania State University, University Park, PA. Available at https://etda.libraries.psu.edu/catalog/14916erc15Google Scholar
Castro e Costa, E and Duarte, JP (2013) Tableware shape grammar. In Stouffs R and Sariyildiz S (eds), Computation and Performance – Proceedings of the 31st eCAADe Conference, Vol. 2. Delft, The Netherlands: Faculty of Architecture, Delft University of Technology, pp. 635–644.Google Scholar
Castro e Costa, E and Duarte, JP (2014) Generic shape grammars for mass customization of ceramic tableware. In Gero, JS (ed.), Design Computation and Cognition DCC'14. Cham, Switzerland: Springer, pp. 437–454.Google Scholar
Castro e Costa, E, Jorge, J and Duarte, J (2019) Comparing digital tools for implementing a generative system for the design of customized tableware. Computer-Aided Design and Applications 16, 803–821.10.14733/cadaps.2019.803-821CrossRefGoogle Scholar
Cicero, S (2014) MatterMachine is the Third Industrial Revolution, Customized for You. Available at https://www.open-electronics.org/mattermachine-is-the-third-industrial-revolution-customized-for-you/ (Retrieved 28 August 2019).Google Scholar
Coxeter, HSM (1989) Introduction to Geometry, 2nd Edn. New York: Wiley.Google Scholar
Cross, N & Design Research Society (eds) (1972). Design Participation: Proceedings of the Design Research Society's conference, Manchester, September 1971. London: Academy Editions.Google Scholar
Duarte, JP (2008) Synthesis Lesson – Mass Customization: Models and Algorithms (Habilitation Candidacy Exam). Lisboa: Faculdade de Arquitectura, Universidade Técnica de Lisboa.Google Scholar
Duarte, JP (2011) Mass customization: models and algorithms. Keynote address presented at the eCAADe 28th International Conference of the Education and Research on Computer Aided Architectural Design in Europe, Ljubljana, Slovenia: Faculty of Architecture.Google Scholar
Floater, MS (2015) Generalized barycentric coordinates and applications. Acta Numerica 24, 161–214.CrossRefGoogle Scholar
Franke, N and Piller, FT (2002) Configuration Toolkits for Mass Customization: Setting a Research Agenda. Munich, Germany: Technische Universität München.Google Scholar
Franke, N, Schreier, M and Kaiser, U (2009) The “I designed it myself” effect in mass customization. Management Science. doi:10.1287/mnsc.1090.1077Google Scholar
Geng, J (2013) Three-dimensional display technologies. Advances in Optics and Photonics 5, 456–535.CrossRefGoogle ScholarPubMed
Heiskala, M and Tiihonen, J (2007) Mass customization with configurable products and configurators: a review of benefits and challenges. In Mass Customization Information Systems in Business, pp. 1–32 Hershey, Pennsylvania, USA: IGI Global.Google Scholar
Hermans, G (2012) A model for evaluating the solution space of mass customization toolkits. International Journal of Industrial Engineering and Management 3, 205–214.Google Scholar
Huang, M and Hudson, A (2013) Sake set creator. Available at www.shapeways.com/creator/sake-set/ (Retrieved 14 February 2013).Google Scholar
Ken Research (2014) Europe ceramic industry outlook to 2018 – rising consolidation and favorable government regulations to lead towards industry transformation (Market Research No. KR228), Ken Research. Available at https://www.kenresearch.com/manufacturing-and-construction/construction-materials/europe-ceramic-market-research-report/554-97.htmlGoogle Scholar
Kyriakou, H, Nickerson, JV and Sabnis, G (2017) Knowledge reuse for customization: metamodels in an open design community for 3d printing. MIS Quarterly 41, 315–332. https://doi.org/10.25300/MISQ/2017/41.1.17CrossRefGoogle Scholar
Miller, GA (1956) The magical number seven, plus or minus two: some limits on our capacity for processing information. Psychological Review 63, 81–97.CrossRefGoogle ScholarPubMed
Mitchell, WJ (1990) The Logic of Architecture: Design, Computation, and Cognition, 1st Edn. Cambridge, MA: MIT Press.Google Scholar
Naik, HSS (2017) Simplifying Solution Space: Enabling Non-Expert Users to Innovate and Design with Toolkits, 1st Edn.New York, NY: Springer Gabler.10.1007/978-3-658-18283-0CrossRefGoogle Scholar
Naik, HS, Velamuri, V and Möslein, K (2016) Simplifying solution space: a multiple case study on 3D printing toolkits. In Proceedings of the 24th European Conference on Information Systems (ECIS), Istanbul, Turkey. Available at https://aisel.aisnet.org/ecis2016_rp/140Google Scholar
Nervous Systems, Inc (2012) Cell Cycle. Available at http://n-e-r-v-o-u-s.com/cellCycle/index.php (Retrieved 9 January 2013).Google Scholar
Norman, DA (2002) The Design of Everyday Things, Reprint Edition. New York, NY: Basic Books.Google Scholar
Piller, FT (2004) Mass customization: reflections on the state of the concept. International Journal of Flexible Manufacturing Systems 16, 313–334.10.1007/s10696-005-5170-xCrossRefGoogle Scholar
Piller, FT, Schubert, P, Koch, M and Moeslein, K (2004) From mass customization to collaborative customer co-design. In Proceedings of the European Conference on Information Systems (ECIS) 2004, Turku, Finland: Association for Information Systems.Google Scholar
Piller, F, Schubert, P, Koch, M and Möslein, K (2005) Overcoming mass confusion: collaborative customer co-design in online communities. Journal of Computer-Mediated Communication 10.10.1111/j.1083-6101.2005.tb00271.xCrossRefGoogle Scholar
Pine, BJ (1993) Mass Customization: The New Frontier in Business Competition. Boston, MA: Harvard Business Press.Google Scholar
Ramaswamy, V and Prahalad, CK (2006) The Future of Competition: Co Creating Unique Value with Customers. London: Penguin Books India.Google Scholar
Sanders, EB-N and Stappers, PJ (2008) Co-creation and the new landscapes of design. CoDesign 4, 5–18.10.1080/15710880701875068CrossRefGoogle Scholar
Saxena, T and Karsai, G (2010) Towards a Generic Design Space Exploration Framework. In 2010 IEEE 10th International Conference on Computer and Information Technology (CIT), pp. 1940–1947. Bradford, UK: IEEECrossRefGoogle Scholar
Sinclair, M, Campbell, RI and Yavari, H (2014) Specification of an additive manufacturing consumer design toolkit for consumer electronics products. In Bohemia E, Rieple A, Liedtka J and Cooper R (eds), Proceedings of the 19th DMI: Academic Design Management Conference. London, UK: DMI, pp. 1065–1088 (© the authors). Available at https://dspace.lboro.ac.uk/dspace-jspui/handle/2134/22118.Google Scholar
Strobbe, T, Pauwels, P, Verstraeten, R, De Meyer, R and Van Campenhout, J (2015) Toward a visual approach in the exploration of shape grammars. AI EDAM 29, 503–521.Google Scholar
Talton, J, Gibson, D, Hanrahan, P and Koltun, V (2008) Collaborative mapping of a parametric design space, Technical report, Citeseer. Available at http://hci.stanford.edu/cstr/reports/2008-01.pdfGoogle Scholar
Talton, JO, Gibson, D, Yang, L, Hanrahan, P and Koltun, V (2009) Exploratory modeling with collaborative design spaces. In ACM SIGGRAPH Asia 2009 Papers, New York, NY: ACM, pp. 167:1–167:10.10.1145/1661412.1618513CrossRefGoogle Scholar
Toffler, A (1971) Future Shock. New York, NY: Bantam Books.Google Scholar
Tseng, MM and Jiao, J (2001) Mass customization. In Salvendy, G (ed.), Handbook of Industrial Engineering. Hoboken, New Jersey: John Wiley & Sons, Inc., pp. 684–709.CrossRefGoogle Scholar
Von-Tycowicz, C, Schulz, C, Seidel, H-P and Hildebrandt, K (2015) Real-time nonlinear shape interpolation. ACM Transactions on Graphics 34, 1–10.CrossRefGoogle Scholar
Walcher, D and Piller, FT (2012) The Customization 500: An International Benchmark Study on Mass Customization and Personalization in Consumer E-commerce. Las Vegas, Nevada: ICON Group International.Google Scholar
Wang, Y, Jacobson, A, Barbič, J and Kavan, L (2015) Linear subspace design for real-time shape deformation. ACM Transactions on Graphics 34, 1–11.Google Scholar
Williams, A (2017) A crash course in Thingiverse customizer. Available at https://hackaday.com/2017/06/27/a-crash-course-in-thingiverse-customizer/ (Retrieved 28 August 2019).Google Scholar
Woodbury, RF and Burrow, AL (2006) Whither design space? AI EDAM 20, 63–82.Google Scholar
Yavari, H (2017) User Perspective on AM-Enabled Mass Customisation Toolkits (Thesis). Loughborough University. Available at https://repository.lboro.ac.uk/articles/User_perspective_on_AM-enabled_mass_customisation_toolkits/9355310Google Scholar
Zhao, H, McLoughlin, L, Adzhiev, V and Pasko, A (2018) 3D Mass customization toolkits design, part I: survey and an evaluation model. Computer-Aided Design and Applications 16, 204–222.CrossRefGoogle Scholar
Zipkin, P (2001) The limits of mass customization. MIT Sloan Management Review 42, 81–87.Google Scholar