1. Introduction
A prepreg is a semi-finished, machine-made product in which a reinforcement material has been impregnated with a pre-catalyzed resin [
1]. It has been traditionally employed in the fabrication of high performance composites which could be used for aerospace components, high performance yachts, racing cars, sports equipments and so on [
2–
4]. As the quality of the prepreg will greatly influence the properties of the composite products, it is important to monitor the quality during the production of prepreg.
In the manufacture of the prepreg, the high sizing content will induce insufficient resin impregnation, while controlling the sizing content of the alkali-free cloth is very important to the impregnation.
In addition, the resin content, the soluble resin content and the volatile content are the key factors to ensure the quality of the prepreg. A low volatiles content could cause aging of the prepreg, while a high resin content could make the resin outflow and be wasted.
The traditional analytic method involves solvent extraction, weighing and burn-off. However, this method is not the best candidate for analyzing the quality of prepreg cloth due to the excessive time required, agent wasted and specimen destroyed. Besides, it can only analyze a small part of the prepreg.
In recent years, many methods such as gamma-ray reflectance [
5], beta-ray transmission [
6–
8] and ultrasonic techniques [
9,
10] have been developed for analyzing the quality of the prepreg. However, these methods can only measure the resin content of the prepreg, the other factors cannot be measured. Further more, these radiant rays are also harmful.
Near-infrared (NIR) spectroscopy is a rapid and nondestructive method for the simultaneous measurement of different constituents in various products. Nowadays, NIR spectroscopy has been successfully applied in analysis process during the manufacture of certain polymers [
11–
17]. In previous work, NIR spectroscopy was applied in the quality determination of phenolic resin prepreg cloth [
18–
19]. It is ideally suited to quality control during the manufacture of prepreg. NIR spectra typically contains unselective, extensively overlapped bands, so it is necessary to use multivariate chemometric analytical tools such as partial least squares (PLS), principal components regression (PCR), multiple linear regression (MLR) for quantification analysis.
The aim of the study is to develop an on-line monitoring method using diffuse reflection nearinfrared spectroscopy for controlling the quality of alkali-free cloth/phenolic resin prepreg during the manufacture. Calibration models were developed by PLS, which were used to analyze unknown samples. The technical parameters could be adjusted quickly with the help of the NIR analysis results.
3. Experimental Section
3.1 Materials
Phenolic resin was provided by Beijing Research Institute of Material and Technology, China. Alkali-free glass cloth was obtained from Nanjing Research Institute of Glass Fibre, China.
3.2 The design of on-line analysis system
The on-line quality monitoring equipment of the prepreg cloth, which consisted of an FT-NIR spectrometer (Bruker Co., Germany), a bracket with a gilded metal plate, a computer and an alarm, is shown in
Figure 7. The non-contact on-line spectrum instrument was assembled between the dry tower and the take up mechanism, a gilded metal plate was placed under the prepreg cloth in order to enhance the diffuse reflectance effect. With the help of the moving bracket, any part of the prepreg could be analyzed easily. The computer combined with the alarm could monitor the quality of the prepreg on-line.
3.3 Manufacture of prepreg cloth and analysis of NIR spectral data
First, the alkali-free cloth was preheated through dry tower (350 °C) in order to remove water and the great mass of surface sizing. The residual sizing content of the alkali-free cloth was analyzed through NIR method non-contact scanning method (
Figure 7).
After preheating, the alkali-free cloth was pulled into the ethanol solution of phenolic resin, the impregnated cloth then went through the nip rollers to meter the cloth to solution ratio. It ran into another dry tower where the excessive solvent was driven off and the prepreg was formed. At this spot (
Figure 1), the NIR spectrum instrument analyzed the resin content, soluble resin content and volatile content of the prepreg cloth by a non-contact scanning method.
In the test, the light from the sources was 17 cm far from prepreg cloth and the facular on it was 25 mm in diameter, the diffuse reflectance spectra from the cloth were recorded by the spectrometer. The NIR spectrometer was operated in the near infrared region from 4000 to 12,000 cm−1 using a tungsten light source and an indium gallium arsenide (InGaAs) detector, along with a CaF2 beamsplitter. The resolution of the spectra is 8 cm−1 and the average scanning times is 8, it is 1 seconds. The instrument was controlled via its bundled software. Multivariate calibration models were constructed and spectral pretreatments were applied using the OPUS/QUANT-2 software (Bruker Co., Germany).
3.4 Chemical analysis
When the spectrometer completed a collection of spectrum, the corresponding piece was cut from the cloth exactly and was regarded as one sample. When the production speed, the concentration of solution, the distance of nip rollers and the temperature of dry tower were changed, different samples were collected which could represent the character of the alkali-free cloth and prepreg cloth.
For the sizing content of the alkali-free cloth, the test specimen W was placed in the oven at 110 °C for 10 min and weighed to the nearest 0.0001 g to obtain the weight WB1, then was placed in a muffle furnace at 600 °C for 15 min and weighed to obtain the weight WB2. The sizing content (S %) was calculated as follows:
For the prepreg sample, the test specimen was divided into two equal parts A and B, and weighed separately to the nearest 0.0001 g to obtain the initial weight WA and WB. Part A was placed in an oven at 160 °C for 10 min, cooled in a desiccator, and immediately weighed to obtain the weight WA1. Part B was dissolved in acetone for 10 min, placed in the oven at 160 °C for 10 min and weighed to obtain the weight WB1, then was placed in a muffle furnace at 600 °C for 10 min and weighed to obtain the weight WB2. The volatile content (V%), the resin content (R %) and the soluble resin content (S %)
were calculated as follows:
3.5 Statistical analysis
PLS regression was used to develop the calibration models. The determination coefficient (R2), root mean square error of calibration (RMSEC) and root mean square error of prediction (RMSEP) were calculated to evaluate the model and find the best model. The linearity correlation coefficients(R) were a measure of the consistency between the NIR prediction values and actual values for the calibration set. Full cross-validation (leave-one-out) was applied to optimize the calibration models. In the optimize process, the number of PLS factors was determined and the outliers were detected. Spectral outliers were detected through the Mahalanobis distance. A spectrum with a Mahalanobis distance larger than the limit was marked as a spectral outlier.
In order to eliminate variations in offset or different linear baselines and instrument noise, to ensure a good correlation between the spectral data and the concentration values, several spectral pretreatments were tested such as straight line subtraction (SLS), vector normalization (VN), min-max normalization (M-MN), multiplicative scattering correction (MSC), first derivative (1stDER), second derivative (2ndDer), 1stDER +SLS, 1stDER +MSC, 1stDER +VN, constant offset elimination (COE).