STUDY OF GRAIN DAMAGE FACTORS IN THE PROCESSES OF SEPARATION
Abstract
A structural model of the grain damage process during separation has been created, and a criterial equation that includes six indicators has been developed. The obtained regression equations allow assessing the damage level based on influential parameters such as impact force, force impulse, modulus of elasticity, density, and grain hull rigidity. It was determined that the greatest influence on damage is exerted by impact force (cubic dependency), force impulse (inverse quadratic dependency), modulus of elasticity and rigidity (logarithmic dependency), and grain density (linear dependency). The obtained relationship is suitable for predicting micro-damage and grain injury if five parameters describing the physico-mechanical properties of the crop are known. Let's consider the physical meaning of the π-criteria using the example of grain damage during separation under force impact. It has been established that the modulus of elasticity characterizes the grain's ability to elastically deform under force. The primary influence on grain damage is the impact force, which has a cubic dependency. The greater the force and grain density and the lower its elastic properties, the more intense the damage will be. It was also determined that the second criterion, π, includes the ratio between the modulus of elasticity and the impact force on the grain. This indicator characterizes the effect of molecular bonding forces under force impact. It reflects the grain's ability to elastically deform while maintaining internal atomic bonds within the limits of elastic deformation. Therefore, if E>F, relaxation occurs in the grain: the system returns to its initial state before the force application, atomic bonds are restored, the grain structure is preserved, and microcracks heal. Conversely, when E<F, atomic bonds break down, cracks begin to develop, and deformation transitions from elastic to plastic, becoming irreversible. It should be noted that for model to accurately reflect the main characteristics of the studied process, each factor included in the model must demonstrate sufficient variability in the context of its impact on L.
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