In the first year of his PhD Owen honed in on his research topic through a series of studies (some of which are ongoing):

• An investigation into the design process and creativity contributions made by existing GD tools and packages. This work showed the domains that GD tools currently inhabit whilst highlighting opportunities and limitations of current computational design methods.
• Exploration into the variability of outcome performance of GD components realised with AM, displaying the challenges related to producing the often complex ouputs from computational design tools.
• A systematic literature review into the contributions of AI in GD as represented in academic literature, demonstrating the need for lightweighting across multiple engineering applications.
• Characterisation of the relative strength-to-weight ratios of solid and hollowed structures, validating the hypothesis that the hollow components perform better.
• Development of a novel method of beam/ strut cross-section optimisation.

Tangential (but often very closely related) to the main work of his PhD, Owen has also worked on several other projects:

• A collaboration with Didunoluwa Obilanade, a visiting researcher from Sweden’s Lulea University of Technology, looking at the use of design artefacts to better understand the considerations necessary when designing for AM.
• Work toward determining the affects of multiple manufacturing constraints upon the performance of components synthesised using GD.
• Further development of previous work done at the University of Bristol into the development of AM-derived low cost microfluidics.

Future Work

Over the coming year Owen aims to develop his work along five principal avenues. First, a more comprehensive study into the relative performance of solid and hollow components, aiming to compare existing simulation results against real world destructive tests. Second, the completion and write-up of the systematic literature review. Third, design of an Autodesk Fusion 360 add-in that facilitates lightweighting by hollowing components. Fourth, mathematical formulation of the cross-section optimiser method to enable extrapolation of the method. Lastly, further development of the cross-section optimiser tool to extend its functionality into 3D truss structures.

Publication Links

• Investigating and Characterizing the Systemic Variability When Using Generative Design for Additive Manufacturing
• Stress concentrations and design for additive manufacturing: a design artefact approach to investigation
• Are generative design tools creative? A characterisation of tools throughout the design process
• What to Share? A Preliminary Investigation into the Impact of Information Sharing on Distributed Decentralised Agent-Based Additive Manufacturing Networks
• Examining the trade-offs in generating designs for many manufacture

Last updated: 10/09/2024