## 2 Materials and Methods
### 3D Printing and Cost Model
Though the productivity may be improved by completely packing/nesting the build volume with different types of products in one batch to increase packing density, particularly in the PBF process, this study focuses on identical products to fully pack the build volume for simplicity. The pre-processing software of different 3D printing processes have an unidentical array of control settings and build parameters, owing to different materials and fusing mechanisms. In principle, a higher layer thickness than the one used in this study may be selected to manufacture the prescribed parts, which would also decrease the print time per batch. However, parts manufactured with an increased layer thickness can yield relatively lower quality and weaker strength. To this end, this study employs standardized 3D printing parameters for both build and support structures prescribed by the original equipment manufacturer (OEM), to confirm process assurance and repeatability of results. The supports were generated using OEM algorithms for the certified material profiles. Figure 1 shows the packing arrangements of products in different build volumes that enable a good build solution. For example, the nasal swabs intended to be manufactured through the PBF process were not stacked over each other, because the consecutive addition of such a long and narrow geometry would increase the risk of an unsuccessful end result in the build. This research opts for a more pragmatic approach, in which the main focus is on the 3D printing process itself, and the in-depth analyses for post-processing steps, sterilization, and cost of delivery are largely simplified. A cost model with five components was used in this research. The 3D printing machine and its maintenance, raw material, labor, overhead, and sterilization are the basic cost components. By employing this model, we estimated the cost of producing and sterilizing four exemplar medical parts, namely face masks, nasal swabs, face shield holders, and Venturi valves, using four 3D printing machines with different raw materials and varying batch sizes. Different batch sizes for an identical 3D printing machine have also been selected, to highlight the effect of batch size on the costs related to the pre- and post-processing. In total, 17 experiments were conducted. The setting of the experiments, the data, and the assumptions used to create the cost models are presented in Tables 3-5. Data regarding the build times and material consumptions for the experiments was extracted from the OEM software of the 3D printing machines, because this was the most accessible means of data. The software were PreForm 2.20.0, GrabCAD 1.40.28.60898, Ultimaker Cura 4.5.0, and 3 DSprint 2.13.0 for Form 2 TM, uPrintTM SE Plus, UltimakerTM 2, and 3 DSystemsTM ProX SLS 500, respectively. The assumptions regarding the cost of procuring the 3D printing machine and the estimated annual maintenance are presented in Table 4. Moreover, the hourly labor cost for the pre- and post-processing of the print job was assumed to be 15 \(\epsilon\) at a minimum hourly wage, and the duration of labor requirement for each print job was estimated through expert opinion. The cost and method of sterilization was extracted from the literature for the 17 experiments, as shown in Table 5. Parts produced from Dental SG and Polyamide 12 can be sterilized by an autoclave, while the ABS parts require submersion in a C3H8O [26] solution. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Exp. ID** & **Product** & **Equipment** & **Material** & **Capacity of Machine** & **Experiment Batch** & **Print** \\  & & & & **Chamber (L)** & **Size** & **Time/Batch (h)** \\ \hline  
\end{tabular}
\end{table} 
Table 3: Experiment settings and model parameters. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Exp.