CAJ | 학술논문

以聚乙烯醋酸乙烯酯（EVA）添加质量分数、甘油添加质量分数、NaHCO3添加质量分数为3个输入量，以淀粉/EVA复合发泡材料熔体的黏度值为输出量，建立了3层BP（back propagation）神经网络模型，并通过毛细管流变仪对复合发泡材料的熔体黏度进行测试，将其正交试验结果作为样本进行训练。研究结果表明，该BP神经网络模型能较为准确地预测复合发泡材料的流变性能；同时发现，随着EVA添加质量分数的增加，复合发泡材料的熔体黏度增加；而随着甘油添加质量分数的增加和NaHCO3添加质量分数的增加，所得复合发泡材料的熔体黏度均下降。
이취을희작산을희지（EVA）첨가질량분수、감유첨가질량분수、NaHCO3첨가질량분수위3개수입량，이정분/EVA복합발포재료용체적점도치위수출량，건립료3층BP（back propagation）신경망락모형，병통과모세관류변의대복합발포재료적용체점도진행측시，장기정교시험결과작위양본진행훈련。연구결과표명，해BP신경망락모형능교위준학지예측복합발포재료적류변성능；동시발현，수착EVA첨가질량분수적증가，복합발포재료적용체점도증가；이수착감유첨가질량분수적증가화NaHCO3첨가질량분수적증가，소득복합발포재료적용체점도균하강。
Using the mass ratio of ethylene-vinyl acetate to EVA, glycerol content and NaHCO3 content as the input parameters, the viscosity as the output parameters, a 3-layer BP (back propagation) neural network was established. The melt viscosity of composite foaming material was tested by capillary rheometer, while the results were taken as samples to forecast the properties of starch foaming materials. The results showed that the BP neural network could predict the properties with fairly good accuracy. Meanwhile, the viscosity of foaming material increased with the increase of EVA content, the viscosity of foaming material decreased with the increase of glycerol content and NaHCO3 content.