# Analysis of Dimensional Tolerances on Hydraulic and Acoustic Properties of a New Type of Prototypal
## 3 Measuring Rig
### Application of Neural Network in Extracting Measurement Points  
The two main software applications used for the analysis were:  
1. [leftmargin=*]
2. AitechSPHINX artificial intelligence software (program with a license purchased for the Faculty of Production Engineering and Logistics, Opole University of Technology, Opole, Poland). The Neuronix module was used for creation of the neural network. \begin{table}
\begin{tabular}{c c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**No.**} & \multicolumn{2}{c}{**Symbol/** & \multicolumn{8}{c}{**Measurement Results—Kzpn**} \\ \cline{3-13}  & **Tolerance** & **Dimension** & **1** & **2** & **3** & **4** & **5** & **6** & **7** & **8** & **9** & **10** \\ \hline
1 & Kzpn1 &
\begin{tabular}{c} 018 \\ (-0.025)–0.035 \\ \end{tabular} & 17.968 & 17.960 & 17.964 & 17.967 & 17.964 & 17.964 & 17.956 & 17.966 & 17.968 & 17.968 \\
2 & Kzpn2 &
\begin{tabular}{c} 018 \\ (-0.025)–0.035 \\ \end{tabular} & 17.968 & 17.964 & 17.968 & 17.968 & 17.970 & 17.962 & 17.964 & 17.968 & 17.968 & 17.967 \\
3 & Kzpn3 &
\begin{tabular}{c} 024.8 \\ (+0.02) \\ \end{tabular} & 24.800 & 24.970 & 24.970 & 24.930 & 24.740 & 24.930 & 24.980 & 24.980 & 24.980 & 24.900 & 24.780 \\
4 & Kzpn4 &
\begin{tabular}{c} 039 \\ (-0.035)–0.009 \\ \end{tabular} & 38.920 & 38.918 & 38.920 & 38.918 & 38.924 & 38.918 & 38.916 & 38.920 & 38.916 & 38.922 \\
5 & Kzpn5 &
\begin{tabular}{c} 11.7 \\ (-0.01) \\ \end{tabular} & 11.697 & 11.694 & 11.692 & 11.699 & 11.705 & 11.702 & 11.698 & 11.698 & 11.694 & 11.692 \\
6 & Kzpn6 &
\begin{tabular}{c} 11.7 \\ (-0.01) \\ \end{tabular} & 11.696 & 11.693 & 11.690 & 11.698 & 11.700 & 11.700 & 11.698 & 11.697 & 11.694 & 11.690 \\
7 & Kzpn7 & W (0.003) & 0.006 & 0.007 & 0.003 & 0.003 & 0.007 & 0.006 & 0.005 & 0.003 & 0.003 & 0.005 \\
8 & Kzpn8 & R (0.005) & 0.005 & 0.003 & 0.005 & 0.005 & 0.005 & 0.003 & 0.005 & 0.000 & 0.005 & 0.000 \\
9 & Kzpn9 & R (0.003) & 0.006 & 0.005 & 0.004 & 0.005 & 0.005 & 0.007 & 0.007 & 0.003 & 0.007 \\ \hline
16 & Kzpn16 & 14.602 & 14.536 & 14.576 & 14.540 & 14.498 & 14.498 & 14.521 & 14.540 & 14.500 & 14.536 & 14.488 \\
17 & Kzpn17 & R & 0.004 & 0.006 & 0.011 & 0.012 & 0.007 & 0.006 & 0.011 & 0.006 & 0.006 & 0.009 \\ \hline \hline \end{tabular} Pr.—perpendicularity.  
\end{table} 
Table 6: Measurement results of the dimensional tolerance for a passive gear—Kzpn [20]. \begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**No. **} & \multicolumn{2}{c}{**Symbol/** & \multicolumn{8}{c}{**Measurement Results—Ht**} \\ \cline{3-13}  & **Tolerance** & **Dimension** & **1** & **2** & **3** & **4** & **5** & **6** & **7** & **8** & **9** & **10** \\ \hline
1 & Ptt1 & F (0.02) & 0.0354 & 0.0435 & 0.0391 & 0.0299 & 0.0220 & 0.0372 & 0.0437 & 0.0407 & 0.0351 & 0.0291 \\
2 & Ptt2 & P (0.03) & 0.0296 & 0.0199 & 0.022. The second, commercially available software used was MATLAB, Opole, Poland [21; 22; 23]. The following types of files were used: training file.lrn, test file.tst, weight file.wgt (Listing 1), knowledge-based file.bw, data file.vts, representation file.pre, multi-task learning system.aut, and chart.vtc. The training file was created for the results of the control measurements (values/tolerances) for the prototype series gear pumps. The measurements included the following elements: body, active gear, passive gear, and plate and set of bearings (Table 4, Table 5, Table 6, Table 7, Table 8). The data elements were assumed to be vectors containing any finite number of coordinates (Listing 2). In contrast, the data on which the learning accuracy of the network was examined was the test set (Listing 3). All measurement points were included [24; 25; 26; 27; 28]. The set contained 1080 data points. The analysis was done for different training/test ratios: 50/50, 60/40, 70/30, 80/20, and 90/10 for three and four layers with one and two hidden layers, respectfully. To calculate the accuracy, in addition, 5- and 10-part cross-validations were performed (Listing 4). The last step is the selection of the best performing model.