Corn is an important economic and food crop, and the corn threshing process is an important link in the processing of corn, but the damage rate in the threshing process has always been a problem, causing difficulties in subsequent processing and storage. To address the high damage rate in corn ear threshing, a texture analyzer was used to measure the fracture force of Boyun 88 and Zhengdan 958 corn varieties in the triaxial direction, and a CT scanning imaging system was used to analyze the connection mode between the carpopodium and the corn cob. The connection between the carpopodium and corn cob, as well as the fracture process of the carpopodium, was simulated. Finally, high-speed photography was used to study the corn ear threshing process. The results indicated that the fracture force of the carpopodium under radial tension was significantly greater than that under axial and tangential shear. Additionally, the simulated fracture stress value of the carpopodium exceeded its actual fracture stress value. Under radial stress, the fracture force between the carpopodium and corn cob exhibited more uniformity on the contact surface. When a tangential load was applied, it was observed that the force chain shifted and dissipated along the axis during corn kernel extrusion. High-speed photography on a discrete test bench revealed that corn kernel dispersion, extrusion, and force transfer facilitated the movement and migration of surrounding kernels, with the force transfer process resembling a “trapezoid”. This study offers theoretical guidance for corn threshing with low damage and an analysis of the threshing process.

The threshing rate is one of the important indexes to evaluate the effect of corn threshing. The weighing method is often used to calculate the depuration rate of maize at present. This method is time-consuming and laborious and can only calculate the overall threshing rate but does not give the threshing rate of individual corn ears. Different parameters of corn ears have complex effects on the threshing rate. By analyzing the threshing rate of each corn ear, we can choose the appropriate ear treatment method, optimize the processing equipment and process flow, and improve the threshing performance. This paper presents a method based on machine vision to detect the threshing rate of corn ears. In this method, machine vision was used to measure the parameters of the corncob and the area of the top of residual kernels. The area of the top of all kernels was restored based on the parameters of the corncob. The threshing rate of corn ears was calculated by the ratio of the area of the top of the missing kernel to the area of the top of all kernels after threshing. A bivariate linear regression area model was established to restore the area of the top of all corn kernels based on corncob parameters. The R² was more significant than 0.98, and the goodness of fit was good. The machine vision inspection results showed that the maximum relative error of length and midsection radius was 7.46% and 5.55%, and the mean relative error was 2.58% and 2.23%. The maximum relative error of the corn ear threshing rate was 7.08%, and the mean relative error was 2.04%. When the residual kernels were concentrated in the midsection, the inspection result of the corn ear threshing rate was better. The maximum relative error was 3.98%, and the mean relative error was 1.07%. This paper provides a new idea and reference for measuring the threshing rate of corn ears.

To address the lack of direct experimental characterization of multi-kernel group threshing during maize ear threshing, an experimental study on maize ear group threshing was conducted based on kernel arrangement unit characteristics. By constructing a torque testing system for maize ear detachment, we analyzed the temporal variation in torque during detachment and its response to different experimental conditions. Statistical evaluation of torque variability and stability was performed using analysis of variance (ANOVA) and error bars. Furthermore, high-speed photography was employed to capture continuous images and analyze the trajectories of kernel motion during critical detachment stages, revealing the movement characteristics and shedding behavior of kernel clusters. The results indicate that the maize ear threshing process does not involve individual kernel detachment but primarily manifests as group threshing behavior with the arrangement unit as the fundamental unit. Furthermore, the characteristics of the variation in threshing torque correspond to the collective detachment process of the kernel group. This study provides direct experimental evidence for the group threshing mechanism of maize ears through both torque statistical analysis and high-speed visualization. These findings offer valuable insights for threshing mechanism research and the optimization of threshing components.

Characteristics and numerical simulation of maize ear threshing under mixed airflow