Generative Design of an Aircraft Part

The capabilities of GD in SE2019 can be demonstrated by using the design of an aircraft flap bearing clamp as an example (Figure 10). Under a conservative load-case, the parameters of the model can be specified for optimisation. The areas of interest and those that require preservation are the bolt holes and the bearing holder (Figure 11). Using the GD toolkit in SE2019 to specify a reduction in the weight of the bracket by 30% whilst maintaining a factor of safety of at least 1.5, renders a drastic change in part geometry (Figure 11). As can be seen in Figure 11 areas of low stress have been removed. GD uses the constraints of the design space to explore a wide variety of possible design permutations (Figure 9). Design combinations are explored through machine learning algorithms that use history-based optimisation techniques to improve on previous solutions and explore the design space. Using traditional CAD tools to manually explore an equal number of different solutions would be significantly more time and effort consuming compared to using GD.