Discovering Optimal Design For Asymmetrical Branch-Like Structural Joint
Focus
Master Dissertation
Supervisor
Matthiew Giulbert
Year
May 2024
In my dissertation, a novel structure design framework, Generative Optimal Design, is proposed to address the need for design flexibility in art and design optimisation in engineering.
Taking advantage of the Design of Experiments (DOE), General Design (GD), and the recent advancement of computational Topology Optimisation (TO), together with Additive Manufacturing (AM). The conceptual block diagram and the corresponding implementation technology of off-shelf optimisation software tools and additive manufacturing hardware systems are presented to realize the proposed design framework.
A minimum material design case study of an asymmetrical structure component, under multiple objective constraints and different load cases, is proposed to verify and validate the Generative Optimal Design Framework.
Optimisation Load Case Setting. (A) Axial Load Case (B) Lateral Load Cases
Joint Optimisation Domain Setting.
The sequence of filtering with both engineering criteria and artistic aspect is also demonstrated to converge to the satisfactory optimal design for additive manufacturing. The recommendation for future research on integrating with advanced Generative AI and Genetic Algorithm are also proposed.
