## 3 Results
However, when evaluating the wear of the blades by weight, a statistically significant difference (95% probability) was found among different manufacturers (Alt. 1 and Alt. 2, \"original\" and Alt. 5, Alt. 2 and Alt. 5, and Alt. 4 and Alt. 5). Statistically, these differences in wear were significant in terms of average wear and wear dispersion intervals (Figure 11). It could be assumed that the uneven wear caused by the chopper design would eventually lead to an imbalance in the chopper shaft. As the chopper output increased, about 50% of the chopper blades were worn more intensively and reached a critical condition. Meanwhile, the other 50% of the blades could still be used. The blade's cutting edge received the main load, causing the highest stresses and wear. Therefore, the teeth's wear (edge rounding) started at the tip and spread away from the cutting edge (or tip, in serrated blades) as the teeth descended. The greater the wear (tip rounding), the greater the angle of contact (close to 90\({}^{\circ}\)) between the plant mass to  
Figure 11: Average wear of blades from different manufacturers and their dispersion intervals. Figure 10: Influence of the mounting positions of the “original” blades on the shaft of the chopper on the average wear. be chopped and the blade, the greater the load on the cutting surface, and the higher the energy consumption. Using the patterns of erosive wear, we assumed that the different blade angle influenced the wear. The falling angle of the straw mass with abrasive dust on the surface created a different intensity of wear [18]. The mechanism of erosive wear by micro\(-\)abrasive particles at an angle of 10-15\({}^{\circ}\) was micro\(-\)cutting [19]. The determined values of wear and blade angle were a bit complicated because of the different hardness of the blades. However, the hypothesis was that the smaller the angle of the sharpened blade, the lower the wear could be raised, based on points with close hardness values (Figure 12, connected with a dotted line). That led to the conclusion that the longer the blade was used, the more its blade would round, and the more it would wear while performing the same amount of work. The wear intensity, depending on the main influencing factors (hardness of the cutting edge of the blade and the angle of the tip), varied according to a quadratic function, which is described by the following equation (95% confidence level) (Figure 13a):  
\[\mathrm{I}=\mathrm{f}(\mathrm{HV},\Theta)=-28.14+0.02731\ \mathrm{HV}+2.123\ \Theta-1.8\cdot 10^{-6}\ \mathrm{HV}^{2}-0.00124\ \mathrm{HV}\ \Theta-0.03027\ \Theta^{2} \tag{1}\]  
where HV is the Vickers hardness of the cutting edge and \(\Theta\) is the blade angle of the blades, in degrees. The correlation plot between the experimental and simulation data (sum of squares of residual errors, \(\mathrm{SSE}=1.664\cdot 10^{-27}\)) is shown in Figure 13b. The study found that the hardest blades had the least wear and, therefore, the highest potential resource. The metallographic study showed that the hardest blade, Alt. 2 (HRC 53), was dominated by a troostite (white arrows, Figure 14b) with a martensite (dotted arrows, Figure 14b) microstructure. On the other hand, the lowcarbon, lowhardness blade, Alt. 5 (HRC 37), had a predominantly fine pearlite (white arrows, Figure 14a) microstructure with troostite (dotted arrows, Figure 14a). The specific features of the hardening process were the low metal mass of the blade, the rapid induction heating accompanied by rapid cooling by radiation, and insufficient time before the start of hardening for a complete structural transformation of the heated steel. Figure 12: The influence of the edge angle \(\Theta\) on the wear of chopper blade I.  
Figure 14: Microstructures of the most and least worn blade steels (100\(\times\) and 500\(\times\)): **(a)** Alt. 5 and **(b)** Alt. 2. Figure 13: Simulation results: **(a)** plot of the dependence of wear I on the main influencing factors (H, \(\Theta\)).