AbstractNew manufacturing methods such as Filament Deposition Modelling (FDM) have the potential to radically change the way in which we produce and consume everyday goods. They democratise manufacturing by enabling users to make functional, useful products. This is achieved without loss of capability and, when compared with traditional mass manufacturing methods, with reduced environmental impact and significantly lower manufacturing costs.
Despite these proven benefits, increased proliferation of these manufacturing technologies is prohibited by a lack of appropriate design tools for everyday users. Correspondingly, there is a need to democratise design for such users. Existing design approaches principally constitute traditional CAD based methods and design repositories. The former offers high design freedoms but high requisite skills, and the latter the reverse with neither approach accommodating the huge design space afforded by FDM.
It is proposed that this could be addressed by using generative design approaches to augment the existing capabilities of design repositories. Correspondingly, this thesis presents an innovative generative design methodology that can be integrated within existing design platforms to greatly expand their capabilities and, in the process, provide design democratisation. It combines a knowledge base of manufacturing parameters, metaheuristic search algorithms and a fusion of activities from virtual and physical design processes – permitting quick iteration and simulation in the virtual space combined with testing and real-life performance validation in the physical.
The methodology is instantiated in the design of three load bearing components and when compared to a CAD based approach it is shown to provide a two thirds reduction in the quantity and difficulty of design steps that a user needs to undertake.
The work presented in the thesis represents a significant step towards the widespread uptake of technologies such as FDM as it enables the design and manufacture of parts with reliable mechanical properties. Further work would involve the extension of the method to other design tasks, and also its implementation within an existing design repository such as Thingiverse where its use can be monitored and evaluated.
|Date of Award||24 Mar 2020|
|Supervisor||Ben J Hicks (Supervisor) & Aydin Nassehi (Supervisor)|
- Design for Additive Manufacture
- Democratisation of Design
- 3D printing
- Generative Design
- Capability Profiling