## 3 Results and Discussion
### Artificial Neural Networks  
The possible influence of the selected surface roughness parameter was considered independently by building an independent network according to the scheme in Table 2. Tables 4-7 present the statistics of the five networks with the best quality for data sets ANN_1, ANN_2, ANN_3, and ANN_4, respectively. The statistical values were determined for all data sets. The network structure was saved as a code composed of four elements: RBF-x-y-z, (RBF--network type; x--number of inputs; y--number of neurons in the hidden  
Figure 10: The effect of nominal pressure on the CLE for lubrication conditions with (**a**) S100+ and (**b**) S300 oils. layer; and z--number of outputs). The quality of the network is determined by the coefficient of determination, R2. In the analysis of the experimental results, a search for the most promising network architecture was conducted. Due to the significant impact of random effects on the final efficiency of the obtained network, comparing the quality of individual models with different architectures cannot be the basis for recognising one particular architecture as'more promising' than another. It often happens that a network with an architecture less suitable for modelling the specific phenomenon under consideration accidentally achieves \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**No.**} & **Architecture** & **Quality of** & **Quality of** & **Quality of** & **Error for** & **Error for** & **Error for** \\  & **of RBF ANN** & **Training** & **Testing** & **Validation** & **Training Set** & **Testing Set** & **Validation Set** \\ \hline
1 & RBF-8-7-1 & 0.747877 & 0.697086 & 0.796668 & 0.001639 & 0.005628 & 0.000335 \\ \hline
2 & RBF-8-9-1 & 0.809854 & 0.866648 & 0.635650 & 0.001280 & 0.003743 & 0.000738 \\ \hline
3 & RBF-8-8-8-1 & 0.397396 & 0.188642 & 0.670846 & 0.003132 & 0.009617 & 0.000299 \\ \hline
4 & RBF-8-7-1 & 0.847235 & 0.987671 & 0.889200 & 0.001050 & 0.001670 & 0.000087 \\ \hline
5 & RBF-8-8-1 & 0.965571 & 0.980861 & 0.521025 & 0.000252 & 0.000435 & 0.000256 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Statistics of the selected network for input data set ANN_3. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**No.**} & **Architecture** & **Quality of** & **Quality of** & **Quality of** & **Error for** & **Error for** & **Error for** \\  & **of RBF ANN** & **Training** & **Testing** & **Validation** & **Training Set** & **Testing Set** & **Validation Set** \\ \hline
1 & RBF-8-8-1 & 0.894587 & 0.974533 & 0.627975 & 0.000743 & 0.002112 & 0.000280 \\ \hline
2 & RBF-8-9-1 & 0.934342 & 0.974127 & 0.323185 & 0.000472 & 0.000778 & 0.000374 \\ \hline
3 & RBF-8-8-1 & 0.874248 & 0.987826 & 0.782566 & 0.000878 & 0.003250 & 0.000074 \\ \hline
4 & RBF-8-8-1 & 0.925234 & 0.982205 & 0.579015 & 0.000535 & 0.001283 & 0.000071 \\ \hline
5 & RBF-8-7-1 & 0.591974 & 0.710101 & 0.346976 & 0.002416 & 0.006794 & 0.000429 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Statistics of the selected network for input data set ANN_4. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**No.**} & **Architecture** & **Quality of** & **Quality of** & **Quality of** & **Error for** & **Error for** & **Error for** \\  & **of RBF ANN** & **Training** & **Testing** & **Validation** & **Training Set** & **Testing Set** & **Validation Set** \\ \hline
1 & RBF-8-8-1 & 0.894587 & 0.974533 & 0.627975 & 0.000743 & 0.002112 & 0.000280 \\ \hline
2 & RBF-8-9-1 & 0.934342 & 0.974127 & 0.323185 & 0.000472 & 0.000778 & 0.000374 \\ \hline
3 & RBF-8-8-1 & 0.874248 & 0.987826 & 0.782566 & 0.000878 & 0.003250 & 0.000074 \\ \hline
4 & RBF-8-8-1 & 0.925234 & 0.982205 & 0.579015 & 0.000535 & 0.001283 & 0.000071 \\ \hline
5 & RBF-8-7-1 & 0.591974 & 0.710101 & 0.346976 & 0.002416 & 0.006794 & 0.000429 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Statistics of the selected network for input data set ANN_1.