聚酯玻璃钢大气老化力学性能BP人工神经网络预报模型的建立
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摘要
目的准确地分析并建立起一个老化模型,探究不饱和聚酯玻璃钢在大气环境中的条件与老化性能的变化联系。方法在气象数据的基础上分别分析不饱和聚酯玻璃钢在不同季节不同大气环境下各种力学性能的变化,利用数学建模的人工智能方法-BP神经网络进行建模。结果实际值与预测值有很好的一致性,模型精确度也较高。结论此预报模型可比较精确地评价不饱和聚酯玻璃钢在大气中的老化行为。
Objective To analyze accurately and establish an aging model to explore conditions of fiberglass reinforced plastic in atmospheric environment and relations in change of its aging properties. Methods Changes of fiberglass reinforced plastic's mechanical properties in different atmospheric environments of different seasons were analyzed based on the meteorological data and a model was established with artificial intelligence method-BP neural network of mathematical modeling. Results The actual value and predicted values were in good agreement and the model accuracy was high. Conclusion The prediction model can evaluate aging behaviors of unsaturated fiberglass reinforced plastic in the atmosphere accurately.
Objective To analyze accurately and establish an aging model to explore conditions of fiberglass reinforced plastic in atmospheric environment and relations in change of its aging properties. Methods Changes of fiberglass reinforced plastic's mechanical properties in different atmospheric environments of different seasons were analyzed based on the meteorological data and a model was established with artificial intelligence method-BP neural network of mathematical modeling. Results The actual value and predicted values were in good agreement and the model accuracy was high. Conclusion The prediction model can evaluate aging behaviors of unsaturated fiberglass reinforced plastic in the atmosphere accurately.
引文
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[2]王禹阶,崔鹏.玻璃钢与复合材料的生产及应用[M].合肥:合肥工业大学出版社,2005.
[3]解昊.聚丙烯无纺布的光老化与降解[D].广州:华南理工大学,2012.
[4]闫永生.不同光源条件下PVC涂层膜材料光氧老化性能研究[D].上海:东华大学,2012.
[5]赵锦灿,杨俊龙.光缆护套的热氧与光氧加速老化研究[J].塑料工业,2015,43(12):87—92.
[6]滕旭秋,费占黎.多因素作用下橡胶沥青老化特性研究[J].兰州交通大学学报,2016,35(4):1—5.
[7]代军,晏华.不同类型聚乙烯光氧老化特性比较研究[J].工程塑料应用,2016,44(8):88—92.
[8]LEE B L,HOLL M W.Effect of Moisture and Thermal Cycling on Inplane Shear Properties of Graphite Fibrereinforced Cyanate Ester Resin Composites[J].Composites Part A:Applied Science and Manufacturing,1990,27(11):1015—1022
[9]王仲昭.材料耐光老化测试研究[J].纺织检测与标准,2016(4):7—11.
[10]周威,文俊.环氧沥青在模拟自然条件下的老化机理研究[J].胶体与聚合物,2016,34(3):122—124.
[11]OTIENO-ALEGO V,CREAGH D,JAR B,et al.Characterization of the Moisture Absorption and Thermal Ageing Behavior of Polymeric Composite Systems Using Raman Spectroscopy[C]//First International Conference on Ageing Studies and Lifetime Extension of Materials,UK:Oxford,1999:113—121.
[12]杜武青,赵晴.XPS研究不饱和聚酯人工加速老化行为[J].工程塑料应用,2010,38(6):60—63.
[13]刘玉兰,韩明哲.红外光谱法在SBS老化评价中的应用[J].分析仪器,2016(5):53—55.
[14]谢红梅,黄伟.塑料自然老化力学性能的人工神经网络预测[J].工程塑料应用,2001,29(12):31—33.
[15]黄伟,仇君.塑料自然老化力学性能的灰色预测[J].广西大学学报(自然科学版),2001,26(4):275—277.
[16]王玉民,宁培森.不饱和聚酯树脂玻璃钢(GFRP)的老化研究进展[C]//塑料制品老化与防老化技术研讨会.广州,2011.
[17]方明.基于BP神经网络的电网工程估价模型研究[J].科技咨询,2008(35):20.
[18]胡雪棉.基于BP神经网络的中国煤炭资源需求预测模型研究[D].太原:山西财经大学,2008.
[19]赵朝凤,令晓明.浅析BP神经网络基本模型的C语言实现[J].通讯技术,2013,46(1):89—91.
[20]殷震.基于BP神经网络的电力变压器内部故障诊断方法研究[D].天津:天津大学,2007.