## 6 Discussion  
The groups took a broad range of approaches to GD setup, as demonstrated in Table 1. Despite this, a general workflow can be seen. Only one of the nine designs relied upon starting geometry and symmetry, whilst one other did not use obstacle geometry, preferring instead to remove geometry from the bolt holes post-generation. All the designs used fixed structural constraints, with again only one group additionally applying frictionless constraints to account for deflection. Similarly, seven of the designs applied surface loads to the free-end mounts, with two of these additionally applying moments to reflect the dynamic tension-compression felt by a cantilever beam as it deflects. The designs with the highest peak loads before fracture (Designs G and H) instead used remote loads to simulate the force being applied at a standoff from the free-end mounts. All the designs used the maximize stiffness objective and provided some leeway for the solver to violate the AM overhang conditions and for the chosen outcome to, therefore, require supports. Designs A*, G, H, and I were all without a manufacturing constraint alongside the AM constraint applied by all the groups. Only Designs D and F did not generate outcomes for multiple build orientations, using their engineering judgment to then select the most appropriate design, demonstrating a healthy skepticism of the outcome performance. The groups chose  
Figure 9: **(A)** Displacement at fracture for each design. (**B**) Displacement at a 250 N load for each design. a range of overhang angles and minimum thicknesses, with no clear correlation between these factors and design performance. Only Design E iterated their outcome, using the output of the first iteration as the starting shape for the second, but no clear performance benefit was noted from this. Designs G and H were the only two to exploit Fusion 360's experimental solver and post-processing, gaining designs with more consistent thicknesses and straighter struts. The range of predicted loads achieved by simulation in this study demonstrates a large variation in the performance of design output by Fusion 360 GD. Design differences are also clear by visual comparison of the outcomes, varying widely in cross-sectional area, strut length, and strut thickness. This variation demonstrates how different approaches to GD use can lead to a wide spread of outcomes. In combination with the range of different setup processes, the variation also implies that the use of GD tools shifts the design problem from structure design to problem setup, transforming the problem but not necessarily reducing complexity. Training is therefore required to reliably gain useful results from GD tools, and design teams aiming to harness GD for high-performance components should consider certification. There has been a lack of focus on the sensitivity of GD outputs in the academic literature. The authors can find no other studies that discuss it. This is a severe omission, as without consideration of the outcome variation and the exact setup used, previous work is irreproducible and, therefore, unscientific. The larger standard deviation of peak loads than predicted loads (471 N compared to 341 N) shows how the use of polymer printing by MEX compounds variability and uncertainty in the performance of GD for AM components. The requirement for many GD outputs to be producible by AM limits the adoption of GD as industrial standards currently prohibit the use of AM in many areas for fear of uncertain material properties producing premature failures. Correlation between the simulation and mechanical testing results, however, provides validation for a simulation-driven design approach. A design engineer using GD tools can rely upon simulation to provide an indication of the relative performance of structures, allowing informed iteration and evaluation ahead of physical prototyping.