苜蓿草粉对45#钢磨损的RBF神经网络预测

Prediction of Wear Volumes to 45 Steel of Alfalfa Powder Based on RBF Neural Network

苜蓿草粉对金属材料的磨损是影响制粒机使用寿命的主要原因,其中转速、负载和粒度是影响磨损量的重要因素。建立了苜蓿草粉对45#钢磨损的RBF神经网络模型,在磨粒磨损试验机上通过改变试验参数进行磨损试验,获得了不同试验参数下的磨损量。以磨损数据作为RBF神经网络的目标样本,对不同试验参数下的磨损量进行了预测。结果表明:模型可较准确地计算转速、负载和粒度对45#钢磨损量的影响规律。

The wear to orthogonal metals of alfalfa powder is the main cause of affecting the endurance of granulator. The rotational speed, load and granularity are main influence factors of the wear of 45 steel. The RBF neural network model used in the forecast of the 45 steel wear volume was established. The 45 steel wear volume was obtained using friction and wear machine under different expermiental parameters. The wear volumes of different expermiental parameters were forecas- ted using RBF neural network. The results indicate that it is feasible to forecast the effect of the rotational speed, load and granularity to 45 steel wear volume.