Difference between revisions of "Material Based Computational Design Strategies"
Abel Maciel (talk | contribs) |
Abel Maciel (talk | contribs) (→Conference Presentation) |
||
Line 19: | Line 19: | ||
=Conference Presentation= | =Conference Presentation= | ||
− | [[File:PDF-Icon.png |Left|50px|link=https://www.dropbox.com/s/zj33qyreup6dmym/DCIO2022_S4-2_NZJQ3625.pdf?dl=0]] Conference | + | [[File:PDF-Icon.png |Left|50px|link=https://www.dropbox.com/s/zj33qyreup6dmym/DCIO2022_S4-2_NZJQ3625.pdf?dl=0]] Conference Presentation Slides. |
=Keywords= | =Keywords= |
Revision as of 21:04, 16 November 2022
DC I/O 2022 Keynote by Dustin White. https://doi.org/10.47330/DCIO.2022.NGWC1201
Abstract
The lecture outlines the past five years of a research-based design practice with an interest how technology, craft, and materials come together in ways that explore the boundaries between design, architecture, and other disciplines. Specifically, the pedagogy of material based computational strategies supporting the integration of form, material, and structure by incorporating physical form- finding strategies with digital analysis and fabrication processes. In this approach material often comes before shape, with material explorations as the premise for making and fabricating, and design decisions that emerge from the results of the material experiments and testing. The work produced by my students and myself seeks to challenge digital technology and fabrication to further the relationship of material to machine and material to design. With the intent to develop and employ novel software techniques that aid in the translation from the virtual world to the physical medias we engage through craft and technology to hybridize design and making. The work presented varies in scale, technique, method, intent, and fabrication processes but is fascinated with thinking though material-based computational design strategies.
Presentation
Conference Presentation
Conference Presentation Slides.
Keywords
Machine Learning, Digital Fabrication, Design Theory
Reference
DOI: https://doi.org/10.47330/DCIO.2022.NGWC1201
Bibliography
- (Autodes