## 2 Related Work  
### Variability in GD  
Previous studies on the use of GD do not consider the effect the chosen problem setup process may have on the generated outcomes. In their 2021 study, [PERSON] et al. aimed to record the GD process for robotic component re-design using Autodesk Fusion 360. Their process of GD setup was documented and justified thoroughly before choosing the outcome with 'nominal [performance] values' [1]. This outcome was then analyzed using Finite Element Analysis (FEA), and the results were used to declare a 92% weight reduction compared to the traditional component. [PERSON] and [PERSON] used Siemens NX to perform a GD study to optimize an automotive suspension A-arm before producing the design using fiber-reinforced AM [2]. Overall, they achieved a 6\(\times\) weight and a 5\(\times\) cost reduction when compared to the traditional, CNC milled Aluminum component. At no point is the impact of the problem setup on the outcome considered in either paper.  
In their 2022 work 'Utilizing Generative Design for Additive Manufacturing', [PERSON] et al. re-designed three pneumatic cylinder mounting brackets using Fusion 360 [3], concluding that the GD tool enabled a roughly 60% weight reduction. Regardless of detailed documentation of the setup process, little explanation is given for some of the choices made, leaving doubt that the setup used would result in a theoretical minimum mass for the component. [PERSON] et al. used the Fusion 360 tool to achieve a similar weight reduction when re-designing the uprights for their Formula Student car [6]. However, despite a considered load case approach, they failed to acknowledge any sensitivity that their results may have to the GD problem setup.  
[PERSON] et al. in 2021 and [PERSON] et al. in 2022 used Autodesk's GD tool to explore alternative avenues for structural joint design [21; 22]. Both groups used the increased design freedoms afforded by AM to realize GD outcomes that minimized mass and reduced maximum displacement and principal stresses. Whilst performance increased and the studies demonstrated that the designs could be produced by AM, neither investigation considered how their results might have been changed if they had defined their problem differently.  
[PERSON] et al. used GD to explore optimal aerospace component re-design, ranking outcomes based upon their mass [23]. Similarly, [PERSON] et al. used Fusion 360's GD package to perform static structural design of a robotic gripper arm [24]. In both studies, a range of materials and manufacturing constraints were considered, and the outcomes were compared. However, the implications of the observed variation are not discussed, and no thought is given to how other setup parameters may have affected the results.  
[PERSON] et al. compared the performance of different GD tools for a bookend design problem, finding a variation of 17% in the mass of the generated outcomes [25]. This investigation acknowledges the variation possible between solvers; however, it again ignores study setup conditions.  
Despite all the studies discussed in this sub-section citing the use of AM as the most appropriate choice for producing GD parts, only [PERSON] and [PERSON], [PERSON] et al., and [PERSON] et al. followed up by using AM to realize their designs [2; 21; 22]. Of these, only [PERSON] and [PERSON] then mechanically tested the part to determine whether the design performed well post-production. This is indicative of the lack of attention given to the whole process of using GD and AM to produce working components. This paper aims to draw attention to this gap to facilitate a better understanding of the effect of the chosen problem setup process on the generated outcomes.