Activities per year
Abstract
This article presents a new design framework for the specification and prototyping of geometrically and behaviorally complex materials with graded properties, coined Predictive Information Modelling (PIM). The contribution is the development of new circular design workflows employing machine learning for predicting fabrication files based on performance and design requirements. The aim is linking endogenous capacities as well as exogenous environmental dynamics of graded materials, as an approach to material focused intelligent design systems. Using two experimental case studies, the research demonstrates PIM as an applied design framework for addressing (1) material uncertainty, (2) multi-scale data integration, and (3) cyclical fabrication workflows. Through the analysis of these models, we demonstrate research methods that are validated for design applications, review their implications, and discuss further trajectories.
Original language | English |
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Journal | TAD - Technology | Architecture + Design |
Volume | 5 |
Issue number | 2 |
Pages (from-to) | 163-176 |
Number of pages | 15 |
DOIs | |
Publication status | Published - Oct 2021 |
Keywords
- Design Intelligence
- Machine Learning
- Natural Fibers
- Graded Materials
- Robotic Fabrication
Artistic research
- No
Activities
- 1 Lecture and oral contribution
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Predictive Information Modelling: Rethinking the Design Model for Material Uncertainty
Vasiliki Fragkia (Lecturer), Isak Worre Foged (Lecturer) & Anke Pasold (Lecturer)
20 May 2022Activity: Talk or presentation › Lecture and oral contribution