The work in this thesis studies some of the potential applications of machine learning in the field of generative design. In particular it looks at how the design process can be automated once sufficient data about the design space has been collected and machine learning used to find the relationship between the design and its properties. The case study chosen for the work is the design of chairs.
Preliminary work was done including the development of a parametric chair modelling program (ChairMaker) that can produce a wide range of chair designs and a series of simulations, including an automated ergonomic model, that were used to find fitness scores for desirable chair properties.
New chair designs were then generated. Initially by using a well-established method; evolutionary design, using decision trees trained on the simulation data as the fitness function. The results were good, with many new viable chair designs produced. A new generative method called the schema method was also developed. It extracts sets of constraints (called schemata) directly from the decision trees and uses these to generate new chairs. The schema method
proved to be extremely efficient at finding viable chairs. Hundreds of diverse, original chairs can be produced within a few seconds. The idea of visual similarity was explored by using the schemata to measure the difference between two chairs. The results showed a remarkably high correlation between the volunteers considering the subjective nature of the task.
The results demonstrate that it is possible to use simulated data and machine learning to make design decisions in generative design. We have shown this through the use of an existing algorithm and an original method. The new method is novel as it uses the learned knowledge about the design space directly to generate designs rather than using a search algorithm.