Prediction of thermal exposure and mechanical behavior of epoxy resin using artificial neural networks and Fourier transform infrared spectroscopy

Publikationstyp
Journal Article
Date Issued
2019-02-19
Sprache
English
Author(s)
Doblies, Audrius  
Boll, Benjamin  orcid-logo
Fiedler, Bodo  orcid-logo
Institut
Kunststoffe und Verbundwerkstoffe M-11  
TORE-DOI
10.15480/882.2112
TORE-URI
http://hdl.handle.net/11420/2188
Journal
Polymers  
Volume
11
Issue
2
Start Page
Art.-Nr. 363
Citation
Polymers 11 (2): 363 (2019)
Publisher DOI
10.3390/polym11020363
Scopus ID
2-s2.0-85069820065
Publisher
Multidisciplinary Digital Publishing Institute
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.
Subjects
non-destructive testing
thermal aging
FTIR
artificial neural network
machine learning
DDC Class
540: Chemie
620: Ingenieurwissenschaften
Lizenz
https://creativecommons.org/licenses/by/4.0/
Loading...
Thumbnail Image
Name

polymers-11-00363.pdf

Size

2.27 MB

Format

Adobe PDF