| 基于BP神经网络的淀粉/EVA复合发泡材料
流变性能预测模型及应用 |
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| 引用本文： | 张 礼,曾广胜,孙 刚,黄 鹤.基于BP神经网络的淀粉/EVA复合发泡材料
流变性能预测模型及应用[J].包装学报,2015,7(3):14-19. |
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| 作者姓名： | 张 礼 曾广胜 孙 刚 黄 鹤 |
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| 作者单位： | 湖南工业大学 包装新材料与技术中国包装总公司重点实验室，先进包装材料与技术湖南省普通高校重点实验室，湖南 株洲 412007 |
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| 基金项目： | 国家科技支撑计划基金资助项目，国家自然科学基金资助项目，湖南省自然科学杰出青年基金资助项目，湖南省科技计划基金资助项目 |
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| 摘 要： | 以聚乙烯醋酸乙烯酯(EVA)添加质量分数、甘油添加质量分数、Na HCO3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Na HCO3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。
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| 关 键 词： | BP神经网络 发泡 熔体黏度 聚乙烯醋酸乙烯酯 流变性能 |
| 收稿时间： | 3/2/2015 12:00:00 AM |
Prediction Model and Application of Starch/EVA Composite Foaming Material
Rheological Property Based on BP Neural Network |
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| Zhang Li,Zeng Guangsheng,Sun Gang and Huang He.Prediction Model and Application of Starch/EVA Composite Foaming Material
Rheological Property Based on BP Neural Network[J].Packaging Journal,2015,7(3):14-19. |
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| Authors: | Zhang Li Zeng Guangsheng Sun Gang and Huang He |
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| Affiliation: | Key Laboratory of New Materials and Technology for Packaging of China National Packaging Corporation,
Key Laboratory of Advanced Materials and Technology for Packaging of Hunan Universities,
Hunan University of Technology,Key Laboratory of New Materials and Technology for Packaging of China National Packaging Corporation,
Key Laboratory of Advanced Materials and Technology for Packaging of Hunan Universities,
Hunan University of Technology,Key Laboratory of New Materials and Technology for Packaging of China National Packaging Corporation,
Key Laboratory of Advanced Materials and Technology for Packaging of Hunan Universities,
Hunan University of Technology and Key Laboratory of New Materials and Technology for Packaging of China National Packaging Corporation,
Key Laboratory of Advanced Materials and Technology for Packaging of Hunan Universities,
Hunan University of Technology |
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| Abstract: | 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. |
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| Keywords: | back propagation neural network foaming melt viscosity ethylene-vinyl acetate copolymer (EVA) rheological property |
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