College of Mechanical and Electronic Engineering, Shandong Agriculture and Engineering University, Jinan 250100, China.
College of Mechanical and Electrical Engineering, Inner Mongolia Agricultural University, Hohhot 010018, China.
Sensors (Basel). 2024 Aug 12;24(16):5217. doi: 10.3390/s24165217.
There is a significant difference between the simulation effect and the actual effect in the design process of maize straw-breaking equipment due to the lack of accurate simulation model parameters in the breaking and processing of maize straw. This article used a combination of physical experiments, virtual simulation, and machine learning to calibrate the simulation parameters of maize straw. A bimodal-distribution discrete element model of maize straw was established based on the intrinsic and contact parameters measured via physical experiments. The significance analysis of the simulation parameters was conducted via the Plackett-Burman experiment. The Poisson ratio, shear modulus, and normal stiffness of the maize straw significantly impacted the peak compression force of the maize straw and steel plate. The steepest-climb test was carried out for the significance parameter, and the relative error between the peak compression force in the simulation test and the peak compression force in the physical test was used as the evaluation index. It was found that the optimal range intervals for the Poisson ratio, shear modulus, and normal stiffness of the maize straw were 0.32-0.36, 1.24 × 10-1.72 × 10 Pa, and 5.9 × 10-6.7 × 10 N/m, respectively. Using the experimental data of the central composite design as the dataset, a GA-BP neural network prediction model for the peak compression force of maize straw was established, analyzed, and evaluated. The GA-BP prediction model's accuracy was verified via experiments. It was found that the ideal combination of parameters was a Poisson ratio of 0.357, a shear modulus of 1.511 × 10 Pa, and a normal stiffness of 6.285 × 10 N/m for the maize straw. The results provide a basis for analyzing the damage mechanism of maize straw during the grinding process.
由于在玉米秸秆破碎加工过程中缺乏准确的模拟模型参数，玉米秸秆破碎设备的设计过程中存在模拟效果与实际效果有较大差异。本文采用物理实验、虚拟仿真和机器学习相结合的方法对玉米秸秆的仿真参数进行标定。基于物理实验测量的玉米秸秆内禀参数和接触参数，建立了玉米秸秆双模态分布离散元模型。采用 Plackett-Burman 实验对仿真参数进行显著性分析，发现玉米秸秆的泊松比、剪切模量和法向刚度对玉米秸秆与钢板的峰值压缩力有显著影响。对显著性参数进行最陡爬坡试验，以仿真试验与物理试验的峰值压缩力的相对误差作为评价指标，发现玉米秸秆泊松比、剪切模量和法向刚度的最优范围区间分别为 0.32-0.36、1.24×10-1.72×10 Pa 和 5.9×10-6.7×10 N/m。利用中心复合设计的实验数据作为数据集，建立、分析和评价了玉米秸秆峰值压缩力的 GA-BP 神经网络预测模型，并通过实验对 GA-BP 预测模型的准确性进行了验证。结果表明，玉米秸秆的理想参数组合为泊松比 0.357、剪切模量 1.511×10 Pa 和法向刚度 6.285×10 N/m。研究结果为分析玉米秸秆在粉碎过程中的破坏机理提供了依据。
