摘要
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。
Neural network technique was employed to study the technology of producing strengthened poplar-veneer composite material.Three mixed resin liquids were selected and impregnated into poplar veneers.With the heating treatment the resins were polymerized and poplar veneers were strengthened.Five parameters including polymeric temperature,polymeric time,the quantity of cobalt naphthenate,zinc chloride and azobisisobutyronitrile were involved in studying the influence of processing technology on conversion loading of the resins.The quantitative relationship between modification process and conversion loading is established based on neural network,and the process parameters were optimized.The optimized values of polymeric temperature,polymeric time,the quantity of cobalt naphthenate,zinc chloride and azobisisobutyronitrile are 80 ℃,7 h,2%,6% and 5%,respectively.The surface hardness,abrasive resistance and static bending strength of the poplar-veneer composite material made of three resins after modification treatment using optimized parameters are increased by 1.34 times,2.35 times and 1.87 times,respectively,compared with that of the un-modified material.
出处
《材料热处理学报》
EI
CAS
CSCD
北大核心
2011年第2期162-166,共5页
Transactions of Materials and Heat Treatment
基金
全国优秀博士学位论文作者专项基金(200764)
国家自然科学基金项目(30700629
30600469)
黑龙江省科技攻关项目(GB03B605)
关键词
杨木单板
复合材料
神经网络
工艺优化
poplar veneer
composite material
neural network
processing technique optimization
