Zirek, Seda;
(2023)
File, Forest, Factory: Genetic Algorithms and Machine Learning with Spatially Varying Micro Properties of Materials and Fabrication Constraints for Digital Design and Making.
Doctoral thesis (Ph.D), UCL (University College London).
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Abstract
This PhD research investigates ways of creating a co-creative and generative design and making environment, based on a strategy similar to biological construction as a series of events enabling a final design, while co-operating with algorithms, materials, and fabrication constraints from an earlier stage for maximised integration. It proposes using algorithms, including genetic algorithms (GAs) and machine learning (ML) with spatially varying micro properties of heterogeneous materials, as a way to design and build, particularly using GAs to design/optimise a solution and ML to learn and generate materials. It is structured around three chapters: File for algorithms, Forest for materials, and Factory for fabrication constraints. The File chapter focuses on instructions and their contemporary version of algorithms. The Forest chapter focuses on materials, in particular wood as an anisotropic material and marble as an isotropic in two different scales of human and microscopic, with discussion of spatial autocorrelation in natural materials and the designing of synthetic microstructures. The Factory chapter concentrates on the selected fabrication method of three-axis computer numeric controlled (CNC) milling machines and investigates ways of integrating and enhancing the fabrication constraints into a co-creative, generative design and making process. The case studies presented throughout the research, first investigate methods to maximise the integration among the elements of design, including instructions, microstructures of materials, and fabrication constraints. Secondly, they explore ways of amplifying the morphological involvement of these elements to maximise co-creativity. While transforming the entire production process into one refined, sophisticated single phase, the thesis creates integrated design pipelines that address the gap and challenge pre-existing forms. In the conclusion chapter, the case studies are analysed qualitatively and quantitatively across eleven specific categories. The performance of each case study is measured and evaluated using Ashby diagrams to assess the trade-offs between these categories and position them in a multi-dimensional space.
| Type: | Thesis (Doctoral) |
|---|---|
| Qualification: | Ph.D |
| Title: | File, Forest, Factory: Genetic Algorithms and Machine Learning with Spatially Varying Micro Properties of Materials and Fabrication Constraints for Digital Design and Making |
| Open access status: | An open access version is available from UCL Discovery |
| Language: | English |
| Additional information: | Copyright © The Author 2023. Original content in this thesis is licensed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0) Licence (https://creativecommons.org/licenses/by-nc-nd/4.0/). Any third-party copyright material present remains the property of its respective owner(s) and is licensed under its existing terms. Access may initially be restricted at the author’s request. |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of the Built Environment UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of the Built Environment > The Bartlett School of Architecture |
| URI: | https://discovery-pp.ucl.ac.uk/id/eprint/10184194 |
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