基于灰色神经网络木塑复合材料动态剪切模量预测

摘要

Based on the characteristics of wood-plastic composite materials processing technology, this paper designed the wood-plastic composite materials processing experiment under different process conditions, and obtained the mechanical properties under different conditions. Based on gray neural network forecasting model, this paper established the prediction model of wood-plastic composite materials dynamic shear modulus. The results show that this model can predict the wood-plastic composite materials dynamic shear modulus well, and the prediction maximum error is 0.69%, which achieves practical engineering requirements.%基于木塑复合材料加工工艺的特点，设计了不用工艺条件下的木塑复合材料工艺实验，并得到了不同条件下的力学性能。本文在灰色神经网络组合预测模型的基础上，建立了木塑复合材料动态剪切模量的预测模型。结果表明：该模型能够较好地预测出木塑复合材料动态剪切模量，预测最大误差为0.69%，能够达到工程实际要求。