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Automatic derivation of design schemata and subsequent generation of designs

Published online by Cambridge University Press:  04 October 2016

Kate Reed  and
Duncan Gillies
Kate Reed*
Affiliation:
Department of Computing, Imperial College London, London, United Kingdom
Duncan Gillies
Affiliation:
Department of Computing, Imperial College London, London, United Kingdom
*
Reprint requests to: Kate Reed, Department of Computing, Imperial College London, London SW7 2AZ, UK. E-mail: k.reed12@imperial.ac.uk
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Abstract

This paper presents a method for automatically generating new designs from a set of existing objects of the same class using machine learning. In this particular work, we use a custom parametric chair design program to produce a large set of chairs that are tested for their physical properties using ergonomic simulations. Design schemata are found from this set of chairs and used to generate new designs by placing constraints on the generating parameters used in the program. The schemata are found by training decision trees on the chair data sets. These are automatically reverse engineered by examining the structure of the trees and creating a schema for each positive leaf. By finding a range of schemata, rather than a single solution, we maintain a diverse design space. This paper also describes how schemata for different properties can be combined to generate new designs that possess all properties required in a design brief. The method is shown to consistently produce viable designs, covering a large range of our design space, and demonstrates a significant time saving over generate and test using the same program and simulations.

Keywords

Automatic DerivationDesign SchemataGeneration of DesignsMachine Learning
Type
Special Issue Articles
Information
AI EDAM , Volume 30 , Special Issue 4: Engineering Design Informatics , November 2016 , pp. 367 - 378
Copyright
Copyright © Cambridge University Press 2016 

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