## 4 Discussion  
The figure below (Figure 3) shows the whole four-level hierarchical structure, in which the calculated weights of the evaluation team, the criteria for the equipment, and, finally, on the fourth level, the weight determining the final order of alternatives can be observed.  
Figure 3: Diagram of multilevel hierarchical structure with values of individual weights.  
\begin{table}
\begin{tabular}{l c c c c c c c c} \hline \hline
**Criterion** & **Weight** & \(\mathbf{a_{1}}\) & \(\mathbf{a_{2}}\) & \(\mathbf{a_{3}}\) & \(\mathbf{a_{4}}\) & \(\mathbf{a_{5}}\) & \(\mathbf{a_{6}}\) & \(\mathbf{a_{7}}\) & \(\mathbf{a_{8}}\) \\ \hline Price (\(f_{1}\)) & 0.0655 & 0.0894 & 0.0204 & 0.2930 & 0.0497 & 0.1252 & 0.1252 & 0.2473 & 0.0497 \\ Accuracy (\(f_{2}\)) & 0.4124 & 0.0266 & 0.0198 & 0.0526 & 0.1292 & 0.2216 & 0.3488 & 0.0640 & 0.1375 \\ Portability (\(f_{3}\)) & 0.1309 & 0.2288 & 0.0326 & 0.3073 & 0.0310 & 0.0310 & 0.0310 & 0.3073 & 0.0310 \\ Sensed area (\(f_{4}\)) & 0.1003 & 0.1163 & 0.1163 & 0.0156 & 0.2009 & 0.0910 & 0.0584 & 0.0157 & 0.3859 \\ Depth of field (\(f_{5}\)) & 0.1118 & 0.0339 & 0.0339 & 0.2928 & 0.0516 & 0.0699 & 0.0204 & 0.2784 & 0.2190 \\ Sensing rate (\(f_{6}\)) & 0.1253 & 0.1305 & 0.1174 & 0.0175 & 0.1714 & 0.0800 & 0.0567 & 0.0165 & 0.4100 \\ Possibility to move with & & & & & & & & & \\ sensed object \& ease of & 0.0537 & 0.2082 & 0.2082 & 0.0334 & 0.0334 & 0.0334 & 0.2416 & 0.0334 \\ implement. (\(f_{7}\)) & & & & & & & & & \\ Weighted sum: & & 0.0898 & 0.0551 & 0.1288 & 0.1098 & 0.1324 & 0.1731 & 0.1306 & 0.1804 \\ Order: & & 7 & 8 & 5 & 6 & 3 & 2 & 4 & 1 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Final product order.  
Due to the fact that the AHP method requires relatively demanding mathematical calculations, and the matrices themselves are larger, a function (see Algorithm 1) in the MATLAB program was used to perform the calculations. The function itself verifies the inverse axiom; let us say that the reciprocity rule \(rji=1/rij\), which, if not met, will print an error message. In the next step, it checks the consistency of the matrix (if it is not consistent, it returns an error message), and, finally, it calculates the required vectors.  
[x,xn] = abh(A), Where x is a proprietary vector of matrix A, xn is a proprietary vector of matrix A (x/sum x) and alfa is a level of consistence function [x,xn] = abh(A,alfa) % Check that the matrix is entered correctly for i = 1:size(A,1) for j = 1:size(A,1) if i= j = 1:size(A,1) if A(j,i) == A(i,j) && A(j,i) == 1; str=[The matrix is misspelled: element a_'....num2 str(i),num2 str(j),  
writing--original draft preparation, J.B. and M.M.; writing--review and editing, J.B.; visualization, J.B.; supervision, J.B. and P.K.; project administration, J.B.; funding acquisition, J.B. All authors have read and agreed to the published version of the manuscript.  
The paper is a part of the research conducted within the project APVV-18-0527 \"Development and optimization of additive manufacturing technology and design of device for production of components with optimized strength and production costs\" funded by the Slovak Research and Development Agency. This paper was completed in association with the project \"Innovative and additive manufacturing technology--new technological solutions for 3D printing of metals and composite materials\", reg. no. CZ.02.1.01/0.0/0.0/17_049/0008407 financed by Structural Funds of the European Union and project. The paper is a part of the research conducted within the project VEGA 1/0665/21 \"Research and optimization of technological parameters of progressive additive manufacturing of effective protective equipment against COVID-19\" funded by the Ministry of Education of Slovak Republic and to the Slovak Academy of Sciences.  
Not applicable.  
The data are available at Slovak University of Technology in Bratislava.  
The authors declare no conflict of interest.