Prediction of Thermal Exposure and Mechanical Behavior of Epoxy Resin Using Artificial Neural Networks and Fourier Transform Infrared Spectroscopy
Prediction of Thermal Exposure and Mechanical Behavior of Epoxy Resin Using Artificial Neural Networks and Fourier Transform Infrared Spectroscopy
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DOI：
10.3390/polym11020363
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发表时间：
2019-02-01
期刊：
影响因子：
5
通讯作者：
Fiedler, Bodo
中科院分区：
文献类型：
--
作者：
Doblies, Audrius;Boll, Benjamin;Fiedler, Bodo
固化环氧树脂和复合材料的热降解检测目前在实践中仅限于严重的热损伤。评估短时间热暴露后的机械性能变化，以及估计热降解聚合物的历史，至今仍然是一个挑战。本文提出了一种利用傅里叶变换红外光谱（FTIR）光谱、数据处理和人工神经网络来准确预测环氧树脂的机械性能以及热暴露时间和温度的方法。因此，环氧树脂已完全固化并暴露于高温不同的时间段。FTIR光谱仪用于测量分子变化，对于膜样品使用中红外（MIR）-FTIR，对于本体样品使用近红外（NIR）-FTIR。热降解膜样品的定量分析显示在FTIR光谱中在MIR范围内的氧化、断链和脱水。使用散装样品的NIR光谱，仅可检测到FTIR光谱的微小变化。然而，使用数据处理，从近红外范围提取分子信息，并使用人工神经网络训练降解模型。即使由于热暴露的变化很小，所提出的模型是能够准确地预测的时间，温度和聚合物的残余强度。
Thermal degradation detection of cured epoxy resins and composites is currently limited to severe thermal damage in practice. Evaluating the change in mechanical properties after a short-time thermal exposure, as well as estimating the history of thermally degraded polymers, has remained a challenge until now. An approach to accurately predict the mechanical properties, as well as the thermal exposure time and temperature of epoxy resin, using Fourier-transform infrared spectroscopy (FTIR)-spectroscopy, data processing, and artificial neural networks, is presented here. Therefore, an epoxy resin has been fully cured and exposed to elevated temperatures for different time periods. A FTIR-spectrometer was used to measure molecular changes, using mid-IR (MIR)-FTIR for film samples and near-IR (NIR)-FTIR for bulk samples. A quantitative analysis of the thermally degraded film samples shows oxidation, chain-scission, and dehydration in the FTIR spectra in the MIR-range. Using NIR spectroscopy for the bulk samples, only minor changes in the FTIR spectra could be detected. However, using data processing, molecular information was extracted from the NIR range and a degradation model, using an artificial neural network, has been trained. Even though the changes due to thermal exposure were small, the presented model is capable of accurately predicting the time, temperature, and residual strength of the polymer.