## 3 Results

Table 3: Selected material parameters used. Figure 8: _Cont_. PLA melts at a relatively low temperature of about 175 \({}^{\circ}\)C; in addition, unlike thermoset materials, PLA can be heated above its melting point repeatedly. It is a tough and cookable material, proven for 50 micron layer heights. The disadvantages of PLA are low heat resistance--PLA parts begin to lose mechanical strength at temperatures above 60 \({}^{\circ}\)C; it also has low UV resistance. PLA is soluble in chloroform or hot benzene, so regular glue can be used to join many parts. PLA is food-safe, but it is not recommended to continuously drink or eat from 3D prints with PLA (over time, bacteria can accumulate in small cracks on the surface of the print, but this can be prevented by applying a food safe coating. PLA postprocessing requires wet sanding. In the simulations on the SLA 3D printer (Prusa SL1S printer), the same model was used in the three variants (not including the original one). The four variants (including the original) were designated PLA100 (the original, at 100% size), PLA50 (50% of the original's size, with a distance between blocks of 0.00005 m (0.05 mm), PLA25, and PLA10. The 50% variant had distances between blocks of 0.00005 m (0.05 mm), the 25% variant had distances between blocks of 0.000025 m (0.025 mm), and the 10% variant had distances between blocks of 0.000001 m (0.01 mm). A different type of support (spreader) had to be used. A support plate (shim) mounted only on the printing table was selected. The 50% and 25% variants were printed correctly. Only the 10% model failed due to the fact that the supports broke through the model. The best results for the ANN were achieved with n = 7, m = 3, and 12 neurons in the hidden layer (i.e., MLP 7-12-3), but we observed that other AR structures were also effective, such as MLP 7-10-3 and MLP 7-14-3. The ANN was able to lower the MSE for the training set to very small values (0.001-0.01) (Tables 4 and 5, best choice in bold). The ANN was able to quickly estimate and identify a solution to the problem of selecting/optimising the 3D-printing parameters as a basic or competitive solution to a \begin{table}
\begin{tabular}{c c c} \hline \hline
**Network Name** & **Quality** & **Quality** \\  & **(Learning)** & **(Testing)** \\ \hline MLP 7-8-3 & 0.8119 & 0.8771 \\ MLP 7-10-3 & 0.8788 & 0.8975 \\
**MLP 7-12-3** & **0.9113** & **0.9212** \\ MLP 7-14-3 & 0.8814 & 0.8858 \\ MLP 7-16-3 & 0.8321 & 0.8714 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Selected ANN quality assessment (the best one is marked in bold). Figure 8: Printouts of the chainmail: (**a**) project, (**b,c**) real chainmail. traditional IT program. For easily programmable tasks with complete and unchecked databases, the first of the above-mentioned solutions can be an advantage. The ANN, however, will work in almost any conditions, providing a better or worse result. Thus, the difference between using a traditional program and an ANN lies in two areas:  
* Database completeness;
* Required accuracy. The results show that the proposed method can provide nonlinear deformation and reflect typical mechanical behaviour (Figures 9 and 10). The chainmail capabilities of an exoskeleton prepared for manual folding were measured as follows:  
* For unidirectional folding: by wrapping on a shaft of specified diameter;
* For bidirectional folding: by wrapping on a sphere with a specified radius. Figure 10: Possibilities of prepared chainmail for hand exoskeleton folding. Figure 9: Chainmail as an element of a hand exoskeleton. The ready-to-use chainmail retained its bending constraint capabilities in the finished product (exoskeleton) (Figure 11).