## 2 Experimental Procedure
### Printing Process Parameter Optimization by Taguchi Analysis  
It has been revealed that many of the properties of Wax3D filament are significantly different from the regular plastics used in FDM manufacturing. Therefore, this circumstance necessitates a search for optimal process parameters for manufacturing wax patterns using an FDM machine. In our case, it was decided to optimize the process parameters for a better dimensional accuracy of printed parts, since dimensional accuracy is considered as the main requirement for IC patterns. Among existing process optimization tools that were previously successfully applied for the FDM process, [PERSON]'s parametric design of the experiment proved to be a popular approach, according to [PERSON] et al. [19]. Although the amount of research conducted on improving the FDM process parameters is immense, most of the works are limited to general plastics such as PLA or ABS, as was pointed out by [PERSON] et al. [19]. As for the wax-based material having a soft nature and low Tm, little to no studies can be found in scientific reports regarding their 3D printing process parameter optimization. Thus, it is important for engineers and manufacturers to know about important process parameters that affect the quality of objects produced using such materials. In [PERSON]'s method, the so-called Orthogonal Array (OA) fractional factorial design is used to create a design matrix with a selected subset of combinations containing numerous factors at several levels. By performing experiments with suggested combinations, one can identify the significant parameters and choose the optimal combination of parameters' levels that yield the desired outcome. One of the key benefits of using [PERSON]'s method is that it allows for the number of experiments to be minimized significantly. Based on [PERSON]'s design, nine experiments were needed to investigate the influence of three parameters with three control levels. As for the parameters, the layer thickness, nozzle temperature, and extrusion velocity were chosen, as shown in Table 3. From Table 3, it can be noticed that the tested temperatures were between 105 and 115 \({}^{\circ}\)C. This is because printing at lower temperatures increases the possibility of emerging issues such as delamination and weak bed adhesion. On the other hand, 3D printing above 115 \({}^{\circ}\)C causes frequent nozzle clog due to heat creep. Similarly, the preliminary experiments showed that the extrusion velocity of values higher than 60 mm/s makes the interlayer bonding weaker due to the quick solidification of the deposited layers. As for layer thickness, the values below 0.2 mm increase the chance of nozzle clogging. The layer thickness above 0.3 mm leads to inferior surface quality. A Minitab software was used to perform the calculations of the Taguchi analysis. It should be noted that the FDM process has a considerable number of controllable parameters to choose from and, therefore, it is difficult to select the most important factors for designing the experiment. To identify the list of parameters, the preliminary work was done through 3D printing numerous simple geometry samples and studying the literature on the subject. The geometry chosen for the analysis was a cube with dimensions of 20 \(\times\) 20 \(\times\) 20 mm (L \(\times\) W \(\times\) H). Other important printing parameters which were kept constant were:  
- bed temperature, 65 \({}^{\circ}\)C;  
- wall count, 2;  
- top/bottom walls, 4;  
- the nozzle diameter, 0.4 mm; and  
- infill pattern, concentric with 80% fill. The dimensions were measured using a digital caliper with an error of \(\pm\)20 \(\upmu\)m. The measured sides were height H (z print direction) and width W (x print direction). In total, 27 parts were manufactured and for each experiment, the average of deviations of three samples was recorded.