## 4 Results  
### Identification of Contact Force in Collision of Wheat Grains with a Flat Metal Surface  
The custom-built test rig was used to record contact-force time profiles, \(F_{c}(t)\) and their maxima, \(F_{c\_max}\), during grain impact on the piezoelectric sensor. Figure 10a-d present \(F_{c}(t)\) for four impact velocities (1.5, 2.5, 3.5 and 4.5 m\(\cdot\)s\({}^{-1}\)) at four moisture levels (7.00 \(\pm\) 0.28%, 10.00 \(\pm\) 0.40%, 13.00 \(\pm\) 0.52% and 16.00 \(\pm\) 0.64%). These moisture ranges were chosen because values below 7% demand disproportionate drying energy, whereas levels above 16% accelerate microbial degradation and are seldom used in practice. Figure 9: Grain velocity versus air velocity in pneumatic ducts [7]. Figure 11a-d show the maximum contact force \(F_{c\_max}\) recorded for 400 wheat-grain impacts performed at four impact velocities (1.5, 2.5, 3.5 and 4.5 m\(\cdot\)s\({}^{-1}\)) and four moisture levels (7%, 10%, 13% and 16%). The experimental values are plotted as blue, filled circles with error bars. Figure 11: Maximum collision force versus impact velocity for wheat grains at different moisture levels: (**a**) 7%, (**b**) 10%, (**c**) 13% and (**d**) 16% [7]. Figure 10: Collision-force \(F_{c}(t)\) profiles for wheat grains at moisture levels (**a**) 7%, (**b**) 10%, (**c**) 13% and (**d**) 16% [7]. To describe the relationship between impact velocity and \(F_{c\_max}\), each dataset was fitted with a power-law model, \(y=ax^{b}\). The solid black curve depicts the fit, while red, dashed Neyman curves delimit the 95% confidence band. This band represents the range in which \(\approx\)95% of future experimental or simulated points are expected to fall, combining the statistical uncertainty of the fit with the natural variability of the grain material. A narrow band denotes high precision and low scatter; a wide band indicates greater uncertainty. Identification of the Relationship Between Maximum Contact Force and Collision Time at Different Moisture Levels  
The relationship between maximum contact force \(F_{c\_max}\) and collision time \(t\) was also analysed for four moisture levels: 7%, 10%, 13% and 16%. Figure 12 shows \(F_{c\_max}\) (N) plotted against \(t\) (us) for wheat grains impacting a flat metal surface; data points are colour-coded by moisture level. At every moisture level, the trend is characteristic of force dissipation: as \(F_{c\_max}\) decreases, contact time increases. The initial decline in force is steep and later tapers off as the collision progresses. The fall in \(F_{c\_max}\) correlates directly with impact velocity. Each series is fitted with a power-law model, \(y=ax^{b}\), whose parameters \(a\) and \(b\) and coefficients of determination (\(R^{2}\)) are listed in Figure 12. Neyman curves indicate the 95% confidence intervals around every fit. The analysis demonstrates that moisture content significantly influences the impact dynamics of wheat grains. Higher moisture levels result in more elastic behaviour, characterised by longer collision times, and lower maximum forces, whereas lower moisture levels produce a more brittle response with a rapid decay of force. The longer contact times observed for grains at 16% moisture correspond to lower peak forces \(F_{c\_max}\) (Figure 12), because their greater plasticity allows deformation to be distributed over time. Figure 12: Relationship between maximum contact force \(F_{c\_max}\) and contact time \((t)\) at different moisture levels: (**a**) 7%, (**b**) 10%, (**c**) 13% and (**d**) 16% [7]. In moist kernels, the increased plasticity and elasticity spread the impact load, yielding lower peak forces. By contrast, grains at 7% moisture are stiffer and less deformable; the collision forces therefore rise rapidly and decay abruptly, producing higher \(F_{c\_max}\) over shorter durations. Wet grains also absorb and redistribute impact energy within the endosperm and outer layers, whereas dry grains concentrate stress locally, amplifying peak forces.