Pattern Making and Learning: Non-Routine Practices in Generative Design (2017)
article⁄Pattern Making and Learning: Non-Routine Practices in Generative Design (2017)
abstract⁄We now witness an upsurge in mainstream generative design tools fortified by simulation that speed up the concealed linear synthesis of optimized design alternatives. In pursuit of optimality, these tools saturate local machines or cloud servers with analysis and design iteration data, only to discard it once the procedure has concluded. Largely absent, however, are tools for an active, adaptive relationship with design exploration and the reuse of corresponding design data and metadata. In Pattern Making and Pattern Learning, we propose that these characteristics are mutually beneficial.This paper presents a series of revisions to the optimization framework for routine design synthesis that examine a potential symbiosis between the production of large datasets big data and nonroutine practices of making in design. Our engagement with iterative design exercises is twofold as a supply of computergenerated design information to foster user intuition and explore the design space on nonobjective terms, and as a supply of humangenerated design information to learn artifacts of user preference in the interest of design software personalization. These concepts are applied to the generation of functionally graded patterning in chair design, combining methods of physical production with programmable sheet material behavior through a custom interactive synthesis framework.
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| Year |
2017 |
| Authors |
Moorman, Andrew. |
| Issue |
ACADIA 2017: DISCIPLINES & DISRUPTION |
| Pages |
426-435 |
| Library link |
N/A |
| Entry filename |
pattern-making-learning |
