Near-Infrared Spectroscopy and Principal Components Regression for the Quality Analysis of Glass/Epoxy Prepreg

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1 Near-Infrared Spectroscopy and Principal Components Regression for the Quality Analysis of Glass/Epoxy Prepreg Near-Infrared Spectroscopy and Principal Components Regression for the Quality Analysis of Glass/Epoxy Prepreg Wei Li*, Wei Jia Gao, Ping Chen, and Bao Lei Sun Liaoning Key Laboratory of Advanced Polymer Matrix Composites Manufacturing Technology, School of Materials Science and Technology, Shenyang Aerospace University, Shenyang , China Received: 2 November 2009, Accepted: 3 August 2010 Summary A near-infrared (NIR) spectroscopic technique associated with a principal components regression (PCR) algorithm has been applied for rapid and nondestructive quality determination of glass/epoxy prepreg. Abundant information of prepreg related with the resin and volatile was observed in the spectra. The raw spectra and first derivative spectra over different spectral ranges were analyzed to obtain the optimum models. The models had correlation coefficients(r)of for the volatile content and for the resin content. The root mean square errors of prediction (RMSEP) for the volatile content and the resin content were 0.316% and 0.666%, respectively. The NIR spectroscopy method can be used to determine the resin content and the volatile content simultaneously, and the prepreg could be analyzed once within 30 s without sample destruction. 1. INTRODUCTION Due to their high performance, fibre reinforced epoxy composites have been widely used in many fields, from aerospace to sporting goods 1,2. Most of these composites are constructed from prepregs. A prepreg is a semi-finished product in which a reinforcement material (fibre or fabric) has been impregnated with a pre-catalyzed resin system 3. Prepregs can provide consistent properties as well as consistent fibre/resin mix and complete wet-out. Prepregs are important intermediates of polymeric composites, and their quality will greatly influence the properties of final composite products. During the manufacturing process of epoxy prepreg, the resin content and the volatile content are often measured by some traditional analysis methods to ensure its quality. However, these methods such as solvent extraction, *Corresponding author. Tel: , Fax: syiae_liwei@126.com Smithers Rapra Technology, 2011 weighing, burn-off and acid digestion 4 are not the most suitable candidates for measuring the quality of prepregs due to the excessive time required, reagent waste and specimen destruction. For this reason, a series of rapid and non-destructive methods have been developed to measure prepreg quality, including gamma-ray reflectance 5, ultrasonic analysis 6, beta-ray transmission 4,7-8 infrared spectroscopy 9 and NIR spectroscopy techniques. Among these techniques, NIR spectroscopy is an excellent analytical method due to its attractive features, such as rapidity, accuracy and non-destructive testing of many components simultaneously. The application of NIR spectroscopy used in the quality analysis and control of phenolic resin prepregs has been realized in our previous work 10-12, and the feasibility of NIR for the quality inspection of carbon/ epoxy resin prepreg has been also studied 13,14. In this present study, a quality determination method of glass/epoxy prepreg cloth was developed by using diffuse reflectance NIR spectroscopy conjunction with principal components regression (PCR). With this objective, the calibration models about the resin content and the volatile content were established respectively, and the optimum models were obtained by using raw spectra or first derivative spectra in different spectral regions. Moreover, the accuracy of NIR method was further verified by measuring some unknown prepreg samples. 2. EXPERIMENTAL 2.1 Materials High strength glass cloth (SW100A- 90a) was supplied by Sinoma Science & Technology Co., Ltd, China. Epoxy resin (E44) was provided by Wuxi Resin Factory of Bluestar New Chemical Materials Co., Ltd., China. Methyl nadic anhydride (methanoisobenzofuran-1,3-dione), which was used as a curing agent, was purchased form Shanghai Nanxiang Reagent Co., Ltd, China. Polymers & Polymer Composites, Vol. 19, No. 1,

2 Wei Li, Wei Jia Gao, Ping Chen, and Bao Lei Sun 2,4,6-tris(dimethyl aminomethyl) phenol, which used as an accelerator, was obtained from Shanghai Chemical Reagent Co., China. Acetone was purchased from Beijing Chemical Reagent Factory, China. 2.2 Collection of Glass/Epoxy Prepreg Cloth Samples The solution impregnation method was used to manufacture the prepreg cloth. The resin solution was prepared by mixing epoxy resin, curing agent, accelerator and acetone according to the proportional ratio. The glass cloth was wetted with resin solution firstly, and then treated in the heating oven to obtain the prepreg cloth. The prepreg samples with different volatile or resin content were obtained by adjusting the solution concentration, the oven temperature or the heating time. Each sample was cut from the prepreg cloth, whose size was mm. A FT- NIR spectrometer (MPA model, Bruker Co., Germany) was used to obtain the NIR diffuse reflectance spectra of the prepreg cloth samples. Using a tungsten light source and an InGaAs detector, NIR spectra were collected over the to 4000 cm -1 with a resolution of 8 cm -1 ; each sample spectrum was achieved by averaging 64 scans. 2.3 Chemical Measurement The resin content and volatile content of prepreg cloth samples were measured according to the standard method currently used. The test specimen was divided into two equal parts A and B, and weighed separately to the nearest g to obtain the initial weight G A and G B. Part A was placed in an oven at 160 C for 10 min, cooled in a desiccator, and immediately weighed to obtain the weight G A1. Part B was placed in a muffle furnace at 600 ºC for 10 min and weighed to obtain the weight G B1. The volatile content (V%) and the resin content (R%) were calculated as follows: V% = (G A G A1 ) / G A 100 (1) R% = (G B (1 V) G B1 ) / (G B (1 V)) 100 (2) The results measured by the chemical analysis method were regarded as the actual values and were used to model the relationship with the NIR spectra. 2.4 Statistical Analysis PCR regression was used to model the relationship between every quality factor of the prepreg cloth and the NIR spectra. In the NIR method, the calibration samples were used to build models and the prediction samples were only used to test the predicted accuracy of models. In order to reflect the model quality correctly by using prediction samples, the property range of the calibration samples was usually slightly bigger than that of the prediction samples. The reason for this is that it was difficult to ensure the prediction reliability if the property range of prediction samples was largely outside the range of the calibration samples. Moreover, if the property range of the prediction samples was far smaller than that of the calibration samples, then the prediction samples would also be unsuitable to test the models quality. On the basis of the above rule, the total of 96 samples was divided into 72 samples for the calibration set and 24 samples for the prediction set. The outliers were detected by the Grubbs test 15. The Mahalanobis distance between the mean spectrum, which shows the average of the spectral features that were present in all the samples, and the spectrum of each sample in the selected analysis region were calculated by using the component content information. The sample with the maximum distance was firstly tested. If the deviation of the sample from the mean was outside a 95% confidence threshold, it was considered to be an outlier and then the sample with the next maximum distance was tested; this process was continued until every sample passed the outlier test. The outliers were forbidden to develop the PCR model and had to be eliminated from the sample sets. The fitted effect of the final PCR models was evaluated in terms of correlation coefficient (R) and root mean square error of calibration (RMSEC), and the predicted accuracy for unknown samples was evaluated by the root mean square error of prediction (RMSEP). R = n n RMSEC = RMSEP = (ŷ i y) 2 (y i y) 2 n m (ŷ i y i ) 2 (3) n (4) (ŷ i y i ) 2 m (5) where ŷ i is the estimated value from the model for sample i, y i is the measured value for sample i, y is the mean of the measured values for all calibration samples, n is the number of the calibration samples and m is the number of the prediction samples. 3. RESULTS AND DISCUSSION 3.1 NIR Spectral Information of Prepreg Samples The raw spectrum of a prepreg cloth sample is shown in Figure 1. It was clearly observed that there were many absorption bands in the NIR spectrum. The absorption of NIR is mainly caused by the vibrational overtones and combination bands associated with hydrogen groups. The C-H, O-H and N-H bands observed are characteristic for polymers in NIR spectra and could be used for various polymer analyses. The prepreg samples comprised epoxy resin, acetone and glass cloth. There 16 Polymers & Polymer Composites, Vol. 19, No. 1, 2011

3 Near-Infrared Spectroscopy and Principal Components Regression for the Quality Analysis of Glass/Epoxy Prepreg were many hydrogen groups in the epoxy and acetone molecules, so the NIR spectra of the prepreg could be interpreted by assignment of the bands to overtones and combination of fundamental vibrations involving hydrogenic stretching and deformation modes 16. The absorption peak at 4065 cm -1 was the combination band of the CH and C-C stretching modes. The combination band of the CH 2 stretching mode and the CH 2 deformation mode appeared at 4320 cm -1. The combination band of the epoxy stretching and deformation modes was observed at 4531 cm -1. The absorption band at 5240 cm -1 was attributed to the first overtone of the OH stretching mode, and the absorption band peak at 5887 cm -1 was attributed to the first overtone of the CH 3 stretching mode. In order to obtain the high quality models, the raw spectra and the first derivative spectra were tested in the model building. From Figure 2, it is clear that the first derivative is useful for revealing peaks that appear as shoulders in the original spectra. The selection of the spectral ranges was also important to the model quality, whose aim was to exclude those regions which give little contribution to the model. The component correlation and variance spectra were used to choose an analysis region or regions. The component correlation spectrum shows the correlation degree between the spectral information and the component contents of all the samples, whereas the variance spectrum shows the spectral variance in the samples. Table 1. Summary of the resin content and volatile content of the prepreg samples Quality factor Sample set Max. Volatile content Resin content Calibration set Prediction set Calibration set Prediction set Min Mean Standard deviation Figure 1. NIR diffuse reflectance spectrum of glass/epoxy resin prepreg cloth From the above results, it was apparent that the NIR spectra contained abundant information related to the resin and the volatile, and a multivariate calibration method based on PCR could be used to extract relevant analytical information and determine the resin content and volatile content. 3.2 Models of Resin Content and Volatile Content The ranges, means, and standard deviations of the resin content and volatile content are summarized in Table 1. In the calibration set, the maximal and minimum volatile contents were 6.555% and 1.940% respectively, and the maximal and minimum resin contents were 42.29% and 28.84% respectively. In the prediction set, the maximal and minimum volatile contents were 6.250% and 2.014% respectively, and the maximal and minimum resin contents were 42.24% and 29.24% respectively. Apparently, the ranges of the results obtained for the calibration samples revealed a broad range of variability, which was important when searching for calibration equations to be used later in the prediction. Figure 2. First derivative NIR spectrum of the prepreg cloth Polymers & Polymer Composites, Vol. 19, No. 1,

4 Wei Li, Wei Jia Gao, Ping Chen, and Bao Lei Sun Figure 3. (a) Correlation spectrum; and (b) variance spectrum of volatiles resulting from the raw spectra Figure 4. (a) Correlation spectrum; and (b) variance spectrum of volatiles resulting from the first derivative spectra The correlation spectrum and variance spectrum of the volatile resulted from the raw spectra are shown in Figure 3. The correlation spectrum exhibits good correlation in the low wavenumber regions and weak correlation in the high wavenumber regions. From the variance spectrum, it is clearly known that it varies sharply in the low wavenumber regions and gives little variation in the high wavenumber regions. From the calibration results of the volatile content (Table 2), it was known that a good model was not developed in the whole region but was developed in the spectral regions which gave good correlation and high variance. When using the first derivative spectra, its correlation spectrum (Figure 4a) shows high correlation only in a few small regions and the variance spectrum (Figure 4b) varies sharply in a few regions also. The significant spectral regions were also chosen according to the above method. The model developed in the , and cm -1 regions gave a better calibration result compared to that developed in the whole region. From Tables 2 and 3, it was known that the best calibration models of volatile content and resin content were obtained in utilizing first derivative spectra and raw spectra respectively. For the volatile content, the best model used nine principal components which explained 99.33% of the spectra variability in its analysis regions, achieving a RMSEC of 0.306% and a RMSEP of 0.316%. For the resin content, the best model was obtained in the cm -1 region; seven Table 2. Effect of spectral ranges to the calibration results of volatile content Spectra mode Spectral range (cm -1 ) R RMSEC RMSEP principal components Spectral variation explained Outliers Raw First derivative , and Polymers & Polymer Composites, Vol. 19, No. 1, 2011

5 Near-Infrared Spectroscopy and Principal Components Regression for the Quality Analysis of Glass/Epoxy Prepreg Table 3. Calibration results of resin content in using raw and first derivative spectra Spectra mode Spectral range(cm -1 ) R RMSEC RMSEP Principal components Spectral variation explained Raw 8000~ First derivative Outliers principal components was used which explained 99.97% of the spectra variability in the analysis region, achieving a RMSEC of % and a RMSEP of 0.666%. No outliers were found in the model building processes. Figure 5. Scatter plots showing correlations between chemical methods and NIR analyses for volatile content The scatter plots showing correlations between the actual values obtained by chemical methods and predicted values by NIR spectrometry for the volatile content and resin content, are exhibited in Figures 5 and 6. In both cases, the actual chemically measured data and the predicted data have good correlation, and many points fall on or close to the unity line. 3.3 Determination of Unknown Samples In order to further evaluate the reliability of the NIR method, 12 unknown samples were collected and their compositions were predicted by the NIR method. The volatile content and the resin content were determined simultaneously within 30 s. The actual measured values and the NIR predicted values are listed in Table 4. The maximal predicted error and the mean absolute error were % and 0.207% for the volatile content, -1.03% and 0.53% for the resin content. From the above results, it is clear that the NIR method developed by using PCR algorithm is capable of quantifying the resin content and volatile content to a reasonable level of accuracy. NIR is a rapid and nondestructive quantitative analytical method once it has been validated. It may be deduced that the NIR spectroscopic method can be used as a quality analysis tool in the manufacture of the glass/epoxy prepreg cloth. Figure 6. Scatter plots showing correlations between chemical methods and NIR analyses for resin content 4. CONCLUSION NIR diffuse reflectance spectroscopy combined with a PCR algorithm has been successfully performed in the quality determination of glass/epoxy prepreg cloth. The correlation spectra and the variance spectra were helpful in the selection of the adaptive spectral regions in the model building. The study showed that the NIR predicted results of volatile content and resin content were reliable. Due to its rapidity and nondestructive characteristics, the NIR method may be an ideal substitute for the traditional chemical analysis method in the quality analysis of glass/ epoxy prepreg. Polymers & Polymer Composites, Vol. 19, No. 1,

6 Wei Li, Wei Jia Gao, Ping Chen, and Bao Lei Sun Table 4. Comparison of actual values with NIR predicted values for unknown samples No. Volatile content Resin content Actual NIR Error Actual NIR Error Mean absolute error ACKNOWLEDGEMENTS This work was financilly supported by Grant No.2007ZF54014 from the Aeronautical Science Foundation of Aviation Industry Corporation of China. The authors thank the Bruker Optics-Beijing for their instrument help. REFERENCEs 1. Soutis C., Progress in Aerospace Sciences, 41, (2005) Cecen V., Sarikanat M., Yildiz H. and Tavman I.H., Polymer Composites, 29 (2008) Banks R., Mouritz A.P., John S., Coman F. and Paton R., Composite Structure, 66 (2004) Huang Y.D., Liu L., Zhang Z.Q., and Wang Y., Composites Science and Technology, 58 (1998) Teagle P.R., Composites, 14, (1983), Anderson M.J., Faull J.A. and Menson S.M., Journal of Advanced Materials, 28 (1997) Molyneux M., Composites, 4 (1984) Huang Y.D., Sun Y.F. and Liu L., Materials Science and Technology, 19 (2003) Moe A.L. and Konicek J.D., US Patent 5,457,319, (1995). 10. Li W., Huang Y. D., Liu L. and Jiang B., Journal of Materials Science, 41 (2006) Li W., Huang Y. D., Liu L. and Chen N. T., Composites Science and Technology, 65 (2005) Li W., Huang Y.D., Liu L. and Jiang B., Polymers and Polymer Composites, 14 (2006) Jiang B. and Huang Y.D., Composites, Part A: Applied Science and Manufacturing, 39 (2008) Jiang B., Huang Y.D., Li W. and Liu L., Iranian Polymer Journal, 16 (2007) Grubbs F.E. and Beck G., Technometrics, 14 (1953) Stark E., Luchter K. and Margoshes M., Applied Spectroscopy Reviews, 22 (1986) Polymers & Polymer Composites, Vol. 19, No. 1, 2011