## 1 Introduction  
As new technologies such as Generative Design (GD) and Additive Manufacturing (AM) are posed as potential revolutions in mechanical engineering, it is important to evaluate the potential of using AM to produce GD parts and whether current approaches can consistently deliver the heavily optimized components that are promised. Whilst previous work has rigorously evaluated GD for AM, little consideration has been given to the sensitivity of the overall process to both the chosen design process and external factors [1; 2; 3]. This study aims to highlight the large range of possible performances when using GD for AM so that further research can follow to increase the reliability of the process, raising the technology readiness level. Simulation-driven GD tools are increasingly providing engineers with the possibility to rethink high-performance components, enabling optimization and wider design space exploration with the ability to return multiple design suggestions that each considers a different combination of constraints such as material and manufacturing method. The chief uses of GD in mechanical engineering and product design are lightweighting, strengthening, and part consolidation [4]. The industrial adoption of this technology is rapidly increasing as its advantages are being recognized. In 2021, Airbus leveraged the advantages of GD to create a cabin partition, achieving a 45% weight reduction compared to the previous traditional design [5]. Similarly, in 2023, [PERSON] et al. used GD to re-design the safety-critical uprights of a car, achieving a weight reduction of 62% without compromising stiffness [6]. GD often utilizes algorithms that produce novel, biological-looking designs and are not compatible with conventional manufacturing methods [7]. Many GD outcomes can therefore only be realized with the use of AM due to the geometric flexibility afforded by layer-by-layer construction [1; 8; 9]. GD and AM are complementary, so as the technologies mature and the desire to adopt the advantages of each drives demand for the other, it is important to identify the consequences of combining the technologies [10]. If one is unfamiliar with such tools, it is easy to suppose that a GD package built upon topology optimization might always return the 'best' structure for the given problem, whether that is the lightest, the one with the fewest parts, or the one with the highest surface area. Besides the fact that it is never possible to guarantee that a global optimum has been achieved [11], the generation of such tools is also tied inextricably to the problem setup provided by the engineer. The tool can only attempt to optimize solutions to the problem that it is given. If the engineer sets the problem up poorly, by unnecessarily removing design freedom, misunderstanding the use case for the component, or setting unreasonable objectives, the GD tool can only return a solution that is limited by this setup. In this way, the use of a GD package transforms the design problem from structural design to GD problem setup, which does not reduce the required understanding to reach a 'good' solution. Traditionally, most high-performance components have been made using either metallics and subtractive manufacturing techniques or composites with often labor-intensive layup processes. AM, meanwhile, provides the potential for rapid prototyping and manufacturing of more complex products and is rapidly emerging as a technology with the ability to produce real, innovative, complex, and robust products [12; 13; 14]. Recently, metal AM has made the foundation for AM to be used in real complex use cases due to comprehensive material characterization [15]; however, capabilities are restricted due to slow production and high equipment and material cost. Recent advances in the Material EXtrusion (MEX) AM of polymers has led to major progress in processing hardware, material science, and Design for Additive Manufacturing (DfAM) in load-bearing applications.