To gain insight into the mechanical properties of crushed sugarcane tail leaves during stress relaxation, a self-made compression equipment was used in this study. The variation law of different factors on the stress relaxation process of crushed cane tail was explored and a stress relaxation model was established. The three-element and five-element generalized Maxwell models were selected to fit the regression analysis of the stress relaxation curve of the crushed cane tail. The comparison showed that the determination coefficient R2 of the five-element stress relaxation model was higher, and a three-factor and three-level response surface test was designed. Following the quadratic regression polynomial of the stress rapid decay time and the equilibrium elastic modulus, the final optimization results obtained are as follows: The moisture content was 60.8%, the crushing particle size was 45 mm, the feeding amount was 150 g, and the stress rapid decay time was 14.0 s. The equilibrium elastic modulus was 129 kPa.
To improve the accuracy of discrete element simulation parameters of sugarcane tail-leaf (STL) feed during dust removal and crushing, this study used a combination of physical tests and EDEM software simulations to calibrate the discrete element simulation parameters of crumbs and dust in the feed. Taking the experimental physical stacking angle (SA) as the response value, the second-order regression models of SA and significant factors were established by Plackett-Burman test, steepest climb test, and Box-Behnken test. Variance analysis and interaction effect analysis were conducted. Taking the accumulation angle of 41.27° obtained by physical experiments as the target value, the significant parameters were optimized. The optimal combination of the following parameters was obtained: tail stem-dust static friction coefficient (SFC) of 0.46, tail leaf-dust coefficient of sliding friction (COSF) of 0.205, JKR surface energy of 0.26, and dust-steel collision recovery coefficient (CRC) of 0.338. Through software simulation verification, the average value was 40.81°, and the relative error of the SA with the physical experiment was 1.13%. The results showed that the calibrated parameters are real and reliable, which can provide a theoretical reference for the design optimization of the straw crushing device, feed processing device, and other related components.
In response to the lack of accurate and reliable parameters in the discrete element simulation analysis of the sugarcane leaf crushing and return device, in this work, the actual and simulated errors of two stacking angles α and β of sugarcane leaves were used as indicators to calibrate the discrete element parameters. The second-order regression models between the important parameters and the indicators were obtained by Plackett-Burman test, steepest climb test, and Box-Behnken optimization test, and the analysis of variance and interaction factors were performed. The response surface method and particle swarm optimization algorithm were used to find the best significance parameters, and the best combination of significance parameters was obtained: the static friction coefficient between sugarcane leaves was 0.306, the rolling friction coefficient between sugarcane leaves was 0.198, and the recovery coefficient of sugarcane leaf-plate collision was 0.102. The relative errors of the simulation results and the physical test stacking angle α and stacking angle β were 0.609% and 1.643%, respectively. The calibration parameters can provide a theoretical reference for the design and research of sugarcane leaf crushing and returning machines, as well as the calibration of discrete element model parameters for leaf crops with high water content.
Discrete element simulation parameters of the tail stem and tail leaves of crushed sugarcane tail leaves (STL) were calibrated by a combination of physical experiments and simulation optimization design. First, the values or ranges of the basic physical parameters and contact parameters of crushed STL were measured using physical tests, and the results were used as the basis for the selection of the simulation parameters. Plackett-Burman testing was applied for the significance screening of the initial parameters. Then, the error values and significant parameters of stacking angle for the second-order regression models were obtained using the steepest ascent experiment and the Box-Behnken optimization test. An analysis of variance (ANOVA) was also performed. Finally, using 37.52° stacking angle of physical test as the validation target, the optimal combination of parameters was obtained: coefficient of static friction (COSF) for tail stem-tail stem of 0.45, COSF for tail leaf-tail leaf of 0.38, coefficient of rolling friction (CORF) for tail stem-tail stem of 0.14, and CORF for tail stem-tail leaf of 0.12. The error of stacking angle obtained from the simulation and the physical tests was 0.976%, which verifies the reliability of the optimal parameters.
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