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利用神经网络建模研究强化杨木单板复合材料的制备工艺。研究中利用三种乙烯类混合树脂浸注杨木单板,通过加热聚合对其进行强化改性,考察聚合温度、加热时间、环烷酸钴加入量、氯化锌加入量和偶氮二异丁腈加入量五个树脂液聚合工艺参数对树脂转化率的影响;并利用人工神经网络构建改性工艺与树脂转化率的关系模型,在此基础上对工艺参数进行优化。结果表明,优化后的聚合工艺为聚合温度80℃,聚合加热时间7 h,环烷酸估2%,氯化锌6%,偶氮二异丁腈5%,此时转化率达到了69.39%;强化后制备的杨木单板复合材料的表面硬度、耐磨性和静曲强度分别至少提高了1.34倍、2.35倍、1.87倍。
Study on the Process of Strengthening Poplar Veneer Composite Using Neural Network Modeling. In the study, poplar veneer was impregnated with three kinds of ethylene-based mixed resins, and its modification was carried out by heating polymerization. The polymerization temperature, heating time, the amount of cobalt naphthenate added, the amount of zinc chloride added, Nitrile addition amount of five resin liquid polymerization process parameters on the resin conversion rate; and the use of artificial neural network to build the relationship between the modification process and the resin conversion rate model, based on which to optimize the process parameters. The results showed that the optimum polymerization conditions were as follows: polymerization temperature 80 ℃, polymerization time 7 h, naphthenic acid 2%, zinc chloride 6%, azobisisobutyronitrile 5%, the conversion reached 69.39% The surface hardness, wear resistance and static buckling strength of poplar veneer composite prepared by strengthening increased at least 1.34 times, 2.35 times and 1.87 times respectively.