Hostname: page-component-cd9895bd7-gbm5v Total loading time: 0 Render date: 2024-12-27T12:54:29.009Z Has data issue: false hasContentIssue false
  • English
  • Français

Synthesising Computational Design Methods for a Human-Centred Design Framework

Published online by Cambridge University Press:  26 May 2022

L. Urquhart ,
A. Wodehouse  and
B. Loudon
L. Urquhart*
Affiliation:
University of Strathclyde, United Kingdom
A. Wodehouse
Affiliation:
University of Strathclyde, United Kingdom
B. Loudon
Affiliation:
Loud1Design, United Kingdom
*
lewis.urquhart@strath.ac.uk

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.

This paper presents models that identify two “cultures” of computational design practice. By reviewing the established culture of computational optimization efforts and contrasting it with the emerging work integrating human-factors into these optimizations, this paper argues that there are sets of key assumptions, outputs and tools that can be synthesized for a generalizable understanding of computational design. Furthermore, this synthesis facilitates the identification of key tools suited to computational design efforts seeking to integrate the complex data associated with human-factors.

Keywords

computational design methodshuman-centred designgenerative design
Type
Article
Information
Proceedings of the Design Society , Volume 2: DESIGN2022 , May 2022 , pp. 633 - 642
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), 2022.

References

Abualigah, L.M., & Diabat, A.T. (2020). A comprehensive survey of the Grasshopper optimization algorithm: results, variants, and applications. Neural Computing and Applications, 1–24.Google Scholar
Ainsworth, T. (2016). Form vs. matter. In Zalta, E. N. (Ed.), The Stanford encyclopaedia of philosophy (Spring 2016 Edition).Google Scholar
Bar, M., & Neta, M. (2006). Humans prefer curved visual objects. Psychological Science, 17(8), 645–648. doi:10.1111/j.1467-9280.2006.01759.xGoogle ScholarPubMed
Beltrán-Fernández, J.A., Andrade, J.L.C., Ochoa, J.C.H., Gómez, L.H.H., Uribe-Cortés, T.B., Garibaldi, P.M. (2021). Numerical–Experimental Study of 3D Printed Ortheses for Rehabilitation of Patients with Musculoskeletal Lesions. In: Beltran-Fernandez, J.A., Öchsner, A. (eds) Design and Simulation in Biomedical Mechanics. Advanced Structured Materials, vol 146. Springer, Cham. 10.1007/978-3-030-65983-7_8Google Scholar
Bendsøe, M. P. and Kikuchi, N. (1988). Generating Optimal Topologies in Structural Design Using a Homogenization Method. Computer Methods in Applied Mechanics and Engineering, Holland.CrossRefGoogle Scholar
Bendsøe, M. P. and Sigmund, O. (1999). Material interpolation schemes in topology optimization. Archive of Applied Mechanics. 69, 635–654 (1999). 10.1007/s004190050248Google Scholar
Bendsøe, M. P. and Sigmund, O. (2004). Topology Optimization Theory, Methods, and Applications. NY, USA, Springer.Google Scholar
Bertamini, M., Palumbo, L., Gheorghes, T. N., & Galatsidas, M. (2016). Do observers like curvature or do they dislike angularity? British Journal of Psychology, 107(1), 154–178. 10.1111/bjop.12132Google ScholarPubMed
Bourdin, B. and Chambolle, A. (2003). Design-dependent loads in topology optimization. ESAIM: Control, Optimisation and Calculus of Variations. 9: 19–48.CrossRefGoogle Scholar
Caetano, I., Santos, L., & Leitão, A.M. (2020). Computational design in architecture: Defining parametric, generative, and algorithmic design. Collection of Frontiers of Architectural Research, 9, 287–300.Google Scholar
Cagan, J., Campbell, M.I., Finger, S., & Tomiyama, T. (2005). A Framework for Computational Design Synthesis: Model and Applications. J. Comput. Inf. Sci. Eng., 5, 171–181.CrossRefGoogle Scholar
Demirbilek, O., & Sener, B. (2003). Product design, semantics and emotional response. Ergonomics, 46, 1346 - 1360.CrossRefGoogle ScholarPubMed
Desmet, P., & Hekkert, P. (2007). Framework of product experience. International journal of design, 1(1).Google Scholar
Giacomin, J. (2014). What Is Human Centred Design? The Design Journal, 17, 606 - 623.Google Scholar
Hermida-Ochoa, J.C., et al. (2021). Tridimensional Design and Printing Techniques to Obtain Personalized Prosthetic Components for Specific Cases Involving Bone Defects. In: Beltran-Fernandez, J.A., Öchsner, A. (eds) Design and Simulation in Biomedical Mechanics. Advanced Structured Materials, vol 146. Springer, Cham. 10.1007/978-3-030-65983-7_7Google Scholar
Li, J., & Tanaka, H. (2018). Rapid customization system for 3D-printed splint using programmable modeling technique – a practical approach. 3D Printing in Medicine, 4.Google ScholarPubMed
Mattheck, C. and Burkhardt, S. (1990). A new method of structural shape optimization based on biological growth. International Journal of Fatigue. 12, 185–190. 10.1016/0142-1123(90)90094-UGoogle Scholar
Noe, R. (2019). Where Does Generative Design Belong? Designers Must Decide [online], Core 77. Available at: https://www.core77.com/posts/89318/Where-Does-Generative-Design-Belong-Designers-Must-DecideGoogle Scholar
Oxman, N. (2010). Material-based design computation. MIT Press, USGoogle Scholar
Oxman, N. (2012). Material computation. In Sheil, B. (Ed.), Manufacturing the bespoke: Making and prototyping architecture (pp. 256–265). Hoboken, NJ, USA: John Wiley & Sons, Inc.Google Scholar
Paoli, A, Neri, P, Razionale, AV, Tamburrino, F, Barone, S. (2020). Sensor Architectures and Technologies for Upper Limb 3D Surface Reconstruction: A Review. Sensors. 20(22):6584. 10.3390/s20226584CrossRefGoogle ScholarPubMed
Pugh, S. (1990). Total Design: Integrated Methods for Successful Product Engineering. Prentice Hall, UKGoogle Scholar
Sansoni, S, et al. . (2015). The aesthetic appeal of prosthetic limbs and the Uncanny Valley: the role of personal characteristics in attraction. International Journal of Design, 9, 67–81.Google Scholar
Shuib, S., Ridzwan, M.I., & Kadarman, A.H. (2007). Methodology of Compliant Mechanisms and its Current Developments in Applications: A Review. American Journal of Applied Sciences, 4, 160–167.Google Scholar
Sigmund, O. and Maute, K. (2013). Topology optimization approaches. Structural and Multidisciplinary Optimization. 48(6): 1031–1055.Google Scholar
Sokolowski, J. and Zochowski, A. (1997). On Topological Derivative in Shape Optimization. Lorraine, France. Technopole de Nancy-Brabois.Google Scholar
Ulrich, Karl & Eppinger, T. (1994). Product Design and Development. McGraw-Hill Inc., USGoogle Scholar
Yildiz, A. R., et al. . (2004). Optimal design of vehicle components using topology design and optimisation. International Journal of Vehicle Design. 34: 387–398.CrossRefGoogle Scholar
Wang, M. Y., et al. . (2003). A level set method for structural topology optimization. Computer Methods in Applied Mechanics and Engineering. 192: 227–246.Google Scholar