摘要
采用均匀设计法和BP神经网络研究了聚丙烯(PP)/乙烯-辛烯共聚物接枝马来酸酐(POE-g-MAH)对聚酰胺6(PA6)的增韧作用,并在此基础上建立了PA6/PP/POE-g-MAH共混物中各组分含量与共混物冲击强度关系的3层BP神经网络预测模型。结果表明,该模型与实验结果基本吻合,可信度较高;当POE-g-MAH含量为14.00%(质量分数,下同)、PP含量为9.00%时,共混物的缺口冲击强度达到92.12kJ/m2。
The research of PP/POE-g-MAH toughening PA6 was conducted by BP neural network and homogeneous design in this paper. On this basis,a 3-layer BP neural network prediction model for the composites between impact strength and the composition was established. The prediction by the model agreed well with the experiments. The impact strength of the toughening PA6 reached 92. 12 kJ/m^2 when the content of POE-g-MAH was 14.00 % and PP content was 9.00 %.
出处
《中国塑料》
CAS
CSCD
北大核心
2011年第12期55-58,共4页
China Plastics
关键词
神经网络
聚酰胺6
聚丙烯
增韧
马来酸酐
neural network
polyamide 6
polypropylene
toughen
maleic anhydride
