## 3 Results
### Fiber Length Evaluation for Molded Samples and Material Pellets  
Using the techniques presented in Sections 2.3, 2.4 and 2.7, the lengths of the reinforcing fibers in the molded samples and in the material pellets were studied. The constructed pipeline [34] allowed for segmenting the photographs obtained by the microscope and recognizing fibers as well as their geometric characteristics. Figure 13 shows example electron microscope images that were further processing.  
The proposed model demonstrated good results in image processing with few fibers and with numerous fibers with overlapping fibers. When calculating the fiber length and diameter, the pipeline recognizes the photo scale (using the Tesseract OCR Engine v0.3.13 [63]) and calculates the necessary characteristics.  
Figure 12: The reverse engineering results of the homogenization mean field models. Material model—solid lines, experimental data—dash lines: (**a**) PA6 GF 30; (**b**) PA6 GF 50; (**c**) PA6 GF 15 THSM; (**d**) PA6 GF 30 THSM.  
Histograms showing the number of fibers extracted from the composite by degradation in a furnace as a function of their length are presented at Figure 14. The histograms for molded samples are shifted to the left, relative to the histograms for material pellets, which makes it possible to evaluate the amount of fiber breaking during the molding process. For the convenience of making such a comparison, the histograms are presented in dimensionless form and normalized relative to the number of measured fibers presented in Table 2.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Material**} & \multicolumn{2}{c}{**Samples**} & \multicolumn{2}{c}{**Pellets**} \\ \cline{2-5}  & \multirow{2}{*}{**Number of Images**} & \multicolumn{2}{c}{**Number of Recognized Fibers**} & \multicolumn{2}{c}{**Number of Images**} & \multicolumn{2}{c}{**Number of Recognized Fibers**} \\ \hline PA6 GF 30 & 43 & 2342 & 30 & \(1681\) \\ PA6 GF 50 & 60 & 5161 & 25 & 3539 \\ PA6 GF 15 THSM & 38 & 2342 & 30 & 1368 \\ PA6 GF 30 THSM & 41 & 2723 & 33 & 3828 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Number of images and recognized fibers for molded samples and material pellets.  
Figure 13: Examples electron microscope images.  
Figure 14 shows histograms constructed using neural networks. The fiber diameters (Table 3) were measured on images with high magnification values (Figure 15). The coefficient of variation (CV) mentioned here and below is the ratio of the standard deviation of the fiber diameter to its mean value.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Material** & \(d\) **Mean, μm** & **CV, \%** \\ \hline PA6 GF 30 & 14.187 & 6.7 \\ PA6 GF 50 & 9.923 & 7.2 \\ PA6 GF 15 THSM & 10.776 & 12.2 \\ PA6 GF 30 THSM & 11.122 & 8 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Fiber diameter measurement.  
Figure 15: Example scanning electronic microscope images for fiber diameter measurement.  
An investigation of the fiber diameters was performed manually using high-magnification images (Figure 15). The results of measuring the diameter of the reinforcing fibers for each material are presented in Table 3.  
To evaluate the aspect ratio distribution, dimensionless histograms were plotted as a function of the fiber fraction in the composite material (Figure 16).  
Based on the obtained histograms, the \(AR_{SEM}\) values were obtained. Table 4 lists the aspect ratio values and effective aspect ratio \(\varphi\) calculation for each of the investigated materials.