## 2 Materials and Methods
### Analysis of Prediction Model  
Outlier analysis was performed using Statistica 14 software to identify and exclude anomalous values from both data columns, focusing on measurements exhibiting significant deviation from the primary dataset (Figure 2). After data filtration, the mean thickness of the deposited layer was calculated using the Statistica 14 software, and the results were tabulated. This process was replicated for measurements obtained from the reference standard to ensure consistency. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Factor Code**} & \multirow{2}{*}{**Variable**} & \multirow{2}{*}{**Unit**} & \multicolumn{5}{c}{**Level of Factors**} \\ \cline{3-6}  & & & \(-\)**2.05464** & \(-\)**1** & **0** & **1** & **+2.05464** \\ \hline x\({}_{1}\) & \(k_{ODM}\) & g\(\cdot\)L\({}^{-1}\) & 15 & 25 & 35 & 45 & 55 \\ x\({}_{2}\) & \(t_{ODM}\) & min & 3 & 6 & 8 & 10 & 13 \\ x\({}_{3}\) & \(T_{ODM}\) & \({}^{\circ}\)C & 40 & 50 & 60 & 70 & 80 \\ x\({}_{4}\) & \(t_{FOSF}\) & min & 1 & 3 & 5 & 7 & 9 \\ x\({}_{5}\) & \(t_{KTL}\) & min & 3 & 4 & 5 & 6 & 7 \\ x\({}_{6}\) & \(U_{KTL}\) & V & 200 & 226 & 250 & 274 & 300 \\ x\({}_{7}\) & \(t_{POLY}\) & min & 13 & 17 & 20 & 23 & 27 \\ x\({}_{8}\) & \(T_{POLY}\) & \({}^{\circ}\)C & 150 & 176 & 200 & 224 & 250 \\ \hline \hline \end{tabular}
\begin{tabular}{c c c} \hline \hline \multicolumn{4}{c}{_\(k_{ODM}\)_—degreasing concentration, \(t_{ODM}\)_—degreasing deposition time, \(T_{ODM}\)_—degreasing temperature, \(t_{FOSF}\)_—debosphating deposition time, \(t_{KTL}\)_—catphoresis voltage, \(t_{POLY}\)_—polymerization time, \(T_{POLY}\)_—polymerization temperature. \\ \end{tabular}
\end{table} 
Table 3: Values of input variable factors. Figure 1: Elcometer 456 (**A**) and 3D-printed device for precise and expeditious measurements of samples (**B**). Figure 2: Filtering of gross errors. The compiled data were further refined by calculating the layer thickness through control measurements conducted on the reference standard. A manufacturer-calibrated foil with a certified thickness of 24.1 \(\upmu\)m served as the benchmark for these measurements. The control measurement value was subtracted from the foil's thickness to determine the deviation, which was then incorporated into the filtered thickness data. This methodology enabled a precise and reliable determination of the average thickness of the deposited layer. Table 4 presents the model suitability analysis and evaluates the influence of various factors on the thickness of the formed cataphoretic layer. The table further reveals that the proportion of variability in the measured thicknesses, represented by the coefficient of determination (R\({}^{2}\)), is approximately 75%. The adjusted coefficient of determination, shown in Table 3, quantifies the model's adequacy in describing the relationship between the input, controlled, chemical, and physical factors and the system's response (i.e., the thickness of the formed layer). This value indicates that 70.79% of the variability is accounted for by the model. Based on the experimentally derived thickness values of the cataphoretic layer, the average thickness (mean of response) is determined to be 17.71 \(\upmu\)m.  
As presented in Table 5, the variability attributable to random error is markedly smaller than the variability in the measurements accounted for by the model. The significance level (Prob > F), derived from the Fisher-Snedecor test, further substantiates the appropriateness of the model. With a chosen significance threshold of \(\alpha=0.05\), the results demonstrate that the model is statistically significant, indicating its reliability in describing the observed phenomena. Based on the lack-of-fit error presented in Table 6, it can be concluded that the significance level is 0.1312, which allows for the acceptance of the null hypothesis at the chosen significance threshold of \(\alpha=0.05\).