Development and validation of a DEM model for predicting compression damage of maize kernels

Abstract

During agricultural production, grain damage is a persistent problem that reduces quality. One of the major types of grain damage is compression damage caused by mechanical harvesting and handling processes. A mechanistic-based model was developed to predict the compression damage of maize kernels using the discrete element method (DEM). Critical model input parameters were determined using a combination of single kernel direct measurements and bulk kernel calibration tests. The Young's modulus was measured with a single kernel compression test and verified with a bulk kernel compression test. An innovative approach was proposed to calibrate the average failure stress using a bulk kernel compression test. After implementation of the model, a validation test was performed using a Victoria mill. Comparing the simulation and the experimental results demonstrated that the simulation gave a good prediction of the damage fraction and the location of the damage when the von Mises stress damage criterion with a variable damage threshold was used. The absolute error between the simulation and experimental results is 0.09. The simulation result indicated that the damage occurred at the inlet and at the section where the pitch of the screw is reduced. A sensitivity analysis was conducted to study the effects of selected model input parameters on the damage fraction. The results showed that irregular shape particles better predicted the damage fraction than single sphere particles. The damage fraction increased with an increase in the Young's modulus or with an increase in the coefficient of friction.