蔡安江,刘亚东,刘俊强,庞秋生.螺旋式布料机输送量的精确预测[J].包装工程,2023,44(13):175-180.
CAI An-jiang,LIU Ya-dong,LIU Jun-qiang,PANG Qiu-sheng.Accurate Prediction of Conveying Volume of Spiral Distributor[J].Packaging Engineering,2023,44(13):175-180. |
| 螺旋式布料机输送量的精确预测 |
| Accurate Prediction of Conveying Volume of Spiral Distributor |
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| DOI：10.19554/j.cnki.1001-3563.2023.13.021 |
| 中文关键词: 螺旋式布料机 输送量 正交实验 预测 BP神经网络 改进灰狼算法 |
| 英文关键词: spiral distributor conveying volume orthogonal experiment prediction BP neural network improved gray wolf algorithm |
| 基金项目:教育部科技发展中心产学研创新基金(2021DZ022) |
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| 中文摘要: |
| 目的 针对预制构件所需布料机输送的混凝土质量问题，以螺旋输送量预测为目标，构建一种以工艺因素和结构因素为输入量的改进灰狼算法优化BP神经网络的输送量预测模型。方法 通过EDEM对布料机输送过程进行仿真模拟，把输送速率和平均质量流率作为正交实验的2种评价标准，并用极差法和矩阵分析法求出螺旋布料机各因素对两标准的影响顺序，并通过混沌种群初始化、步长更新公式及自适应收敛因子的方法改进灰狼算法收敛速度慢和限于局部最优解的问题。结果 通过改进灰狼算法优化BP神经网络的权值和阈值，优化后的输送量预测结果的平均绝对百分误差为8%、决定系数为0.95，比其他实验对比组的输送量模型预测值误差更小。结论 研究可为预制构件的定量布料提供混凝土设定目标值。 |
| 英文摘要: |
| The work aims to take prediction of screw conveying volume as the target, and propose an improved gray wolf algorithm to optimize the conveying volume prediction model of BP neural network with process factors and structural factors as input, to solve quality issue of concrete conveyed by the distributor required for prefabricated components. The conveying process of the distributor was simulated by EDEM, and the conveying rate and the average mass flow rate were used as two evaluation standards of orthogonal experiment. The range method and matrix analysis method were used to obtain the effect order of various distributor factors on the two standards. The methods of chaotic population initialization, step size update formula and adaptive convergence factor were used to improve the slow convergence speed of gray wolf algorithm and the limitation to local optimal solution. The weights and thresholds of BP neural network were optimized through the improved algorithm. For the optimized conveying volume prediction, the average absolute percentage error was 8%, and the coefficient of determination is 0.95, which was smaller than the predicted value error of the conveying volume model of other experimental comparison groups. The study can provide concrete setting target values for the quantitative distribution of prefabricated components. |
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