Wheat straw/polypropylene composites are green recycled and biomass-based material. After accelerated aging test of the composite was done, practical and effective methods for characterization and extraction of texture feature of microscopic Scanning Electron Microscopy (SEM) images of composites were investigated in this paper, and involved data compression and classification recognition were studied as well. Through Angle Measure Technique (AMT) method, the complexity spectra, MA spectra, of the preprocessed SEM images of the composites were derived and then the first four principal components of MA spectra using Principal Component Analysis (PCA) were extracted accordingly. Two kinds of classifiers based on Extreme Learning Machine (ELM) and Support Vector Machine (SVM) were introduced to classify the SEM images into five different aging periods in this paper. The research results indicate that AMT method is a very novel and effective approach in texture feature characterization and analysis of SEM images of composites and high classification accuracy of SEM images in different aging periods by using intelligent recognition can be reached.
Wheat straw/polypropylene composites are a new biomass-based material substituting for the traditional wood/plastics composites (WPCs) in many fields, which can be prepared by using plastic or rubber as matrix and wheat straw plant fiber, a usual agricultural residue, as filler. Compared with the crude wood, these kinds of wood/plastics composites have many advantages, such as better water resistance and anticorrosive property. Although the composites are green, recycled, environmentally friendly material, they would exhibit aging phenomenon unavoidably [
Angle Measure Technique (AMT) method, first proposed by Andrle in 1994, was used for quantitative characterization of curve complexity of geomorphic line [
In the accelerated aging test, SEM images of the composites in 5 different aging periods were shot, and then AMT spectra of texture feature of microscopic SEM images of composites were derived by AMT method and low-dimension reduced spectra data were extracted by PCA. According to the extracted feature matrix, the microscopic SEM images of composites in different aging periods were recognized and classified based on two kinds of intelligent classifier, Extreme Learning Machine (ELM) and Support Vector Machine (SVM). Our investigation can provide a new way for quantitative characterization and depiction of microscopic surface topography of SEM images of composite material in its aging test and precisely determine the composites’ aging stage and indirectly reflect corresponding mechanical, physical, and chemical properties accordingly by using intelligent recognition and classification.
Raw materials of sample preparation of wheat straw/polypropylene composites were shown in Table
Raw materials of the experiment.
Main raw material | Trade name | Origin or manufacturer |
---|---|---|
Polypropylene (film) | — | Jiangyin Oriental Plastic Packaging Co., Ltd., China |
Wheat straw | — | Tanghuang, Jiangyin, Jiangsu, China |
Silane coupling agent | KH550 | Shanghai Yaohua Glass Co., Ltd., China |
Zinc stearate | Industrial grade | Shanghai Yan'an Grease Chemical Plant, China |
Wheat straw/polypropylene composites were prepared by mixing molding method first; mass fraction of wheat straw powder, zinc stearate, and polypropylene was 50%, 1.5%, and 48.5%, respectively. Then, the specimen of wheat straw/polypropylene composites was made by hot press molding with the size of 120 mm × 100 mm × 15 mm. According to the Chinese National Standard GB/T16422.3-1997 UV accelerated aging test for wheat straw/polypropylene composites was conducted. In the aging test, UV-A340 xenon lamp was used, and the temperature of blackboard and condensation was set to 60°C and 50°C, respectively. One aging cycle is 12 h (including illumination, 8 h, and condensation, 4 h), and every 20 cycles were sampled once totaling 100 cycles (i.e., 1200 h). The 5 different aging periods, respectively, correspond to the 20th, 40th, 60th, 80th, and 100th aging period [
Before shooting SEM pictures of composites sample, we need to spray gold on them first. SEM photos of microscopic surface topography of the sample bars were acquired using JSW-6300 Scanning Electron Microscope with accelerating voltage 20 kV, and photo magnification is 50x, 100x, and 200x, respectively. Then, after carefully observing the typical region of interest, we carefully chose and took four SEM photos of the microscopic surface topography of specimen of composites in five different aging periods with the original size of 1232 × 912 pixels for each magnification.
After microscopic SEM photos of the composites were shot in five different aging periods, ROI (region of interest) images or image subsamples at the same size (400 × 400 pixels) were selected from the SEM raw images at the magnification of 100x for the following textural feature extraction and analysis based on AMT method. For each aging stage, 16 preprocessed subsamples of the SEM images (400 × 400 pixels) of material specimens were chosen out elaborately for classification prediction (i.e., totally 80 subsamples in five different aging periods).
For the entire samples, cross validation was introduced to help construct predictive models and then to validate the models in this work, through which we can assess how the results of the predictive models will generalize to a separate test dataset subsequently (to keep the classification models from overfitting).
The calculating procedure of AMT method is described below: for a 1D measurement series or 1D digital curve (2D measurement series should be converted to 1D ones first), in each scale factor
Schematic diagram of AMT method.
The production of AMT spectra of SEM images of composites in different aging periods was evaluated using the software ImageJ (NIH, USA, Version 1.46) with AMT plugin script ( Mean Angle measuring is done using AMT linear method. Minimum scale radius is 1 pixel, maximum scale radius is 500 pixels, and scale increment is 1 pixel. Total number of random sampling points is 5000 points. Unfolded type adopts “spiral” (outside to inside way) converting 2D images to 1D digitalized curve. Number of sample pixels to unfold selects all. Correction option is considered and used to digitize the line, and more information about this choice can be found in [
AMT spectra (refer to MA spectra commonly used) of SEM images of wheat straw/polypropylene composites in different aging periods have distinct texture features with the extension of material aging cycles (Figure
ELM is superior to the traditional feed forward neural network in many aspects, such as classification, regression, and artificial intelligence [
The input of ELM neutral network is the first four principal components of MA spectra of the SEM image of composites extracted by PCA. Programming of ELM classification prediction of five different aging stages was realized in MATLAB (MathWorks, USA, Version R2010a), and the program of classification includes two main functions, that is, ELM training function and ELM prediction one [
Flow chart of ELM algorithm on predicting classification.
SVM is a new data mining technology which is very suitable for small sample statistical analysis and can change the nonlinear classification problem into linear classification one by high-dimensional space transformation [
Flow chart of SVM algorithm on predicting classification.
System overall diagram of texture feature extraction and classification of SEM images of the composites was given in Figure
System overall diagram of texture feature extraction and classification of SEM images of the composites.
SEM image samples of the composites in 5 different aging periods: (a) in 20th aging period; (b) in 40th aging period; (c) in 60th aging period; (d) in 80th aging period; and (e) in 100th aging period.
MA spectra of SEM image samples of the composites in 5 different aging periods.
For each of the five different aging periods, one image sample which has relatively typical characteristics of aging was picked out and illustrated. SEM image samples of the composites in five different aging periods are shown in Figure
From Figure
From Figure
PCA analysis was applied to the exported MA spectra of all SEM image subsamples of the composite, and the first two principal components are good enough to explain 97.4% variance of the samples (PC1 = 86.5%, PC2 = 10.9%). Figure
PCA scores plot of AMT spectra of SEM image samples of the composites in five different aging periods.
The main parameters of program implementation of ELM classification were as follows: (1) Application type of training function is set to classification recognition; (2) “train_label” takes values from “1” to “5” (corresponding to the five different aging periods; namely, train label of 20th aging period of material SEM images is labeled “class1”, train label of 40th aging period of material SEM images is labeled “class 2,” and so on); (3) type of hidden layer activation function chooses “sig” (Sigmoid function); (4) neurons number of hidden layer was set by cross validation approach after program repetitive running. From repetitive running classification accuracy results of classifiers based on ELM neural network can reach relatively high value when the number of hidden layer neurons approached 17 finally. In order to verify every category classification precision, ROC (Receiver Operating Characteristic) curves of ELM predictive model were used here to evaluate the sensitivity and
ROC curves of ELM classification model of SEM image samples of the composites in five different aging periods.
Estimated (black) and cross validation (grey) ROC of class 1
Estimated (black) and cross validation (grey) ROC of class 2
Estimated (black) and cross validation (grey) ROC of class 3
Estimated (black) and cross validation (grey) ROC of class 4
Estimated (black) and cross validation (grey) ROC of class 5
Sensitivity and
Sensitivity and 1 − specificity values of classification model using ELM.
Image sample | Sensitivity | 1 − specificity | ||
---|---|---|---|---|
Calibration | Cross validation | Calibration | Cross validation | |
Class 1 | 1.000 | 1.000 | 0 | 0 |
Class 2 | 0.938 | 0.688 | 0 | 0.016 |
Class 3 | 0.938 | 0.813 | 0.016 | 0.031 |
Class 4 | 0.938 | 0.875 | 0.016 | 0.109 |
Class 5 | 1.000 | 0.938 | 0 | 0 |
The important input parameters of SVM class function were set as follows: (1) “train_label” takes five different values just like the ones in the training function of ELM; (2)
ROC curves of SVM classification model of SEM image samples of the composites in five different aging periods.
Estimated (black) and cross validation (grey) ROC of class 1
Estimated (black) and cross validation (grey) ROC of class 2
Estimated (black) and cross validation (grey) ROC of class 3
Estimated (black) and cross validation (grey) ROC of class 4
Estimated (black) and cross validation (grey) ROC of class 5
Sensitivity and
Sensitivity and 1 − specificity values of classification model using SVM.
Image sample | Sensitivity | 1 − specificity | ||
---|---|---|---|---|
Calibration | Cross validation | Calibration | Cross validation | |
Class 1 | 1.000 | 1.000 | 0 | 0 |
Class 2 | 0.942 | 0.813 | 0 | 0.016 |
Class 3 | 0.961 | 0.875 | 0 | 0.047 |
Class 4 | 1.000 | 1.000 | 0 | 0.016 |
Class 5 | 1.000 | 1.000 | 0 | 0 |
In order to evaluate and compare the predictive ability and performance of two classification models as a whole, statistics results of overall classification accuracy of model using ELM and SVM are reported in Table
Statistics results of overall classification accuracy of model using ELM and SVM.
Classifier type | Calibration dataset | Validation dataset | ||
---|---|---|---|---|
Standard deviation | Average | Standard deviation | Average | |
ELM | 6.3% | 91.5% | 7.2% | 86.3% |
SVM | 4.7% | 93.8% | 5.6% | 92.5% |
From Tables
Relating to the overall classification results, reported in Table
With regard to the first classifier, from the program running results of the classification using ELM neural network, it can be known that this classifier can achieve acceptable classification accuracy. In ELM neural network classification, the number of hidden layer neurons of training function is a very important parameter, and with regard to ELM classifier this parameter value mentioned above has a very significant impact. It showed that through training function repeatedly running as the number of the hidden layer neurons was close to 17 relatively high classification accuracy can be achieved. However, continuing to increase the number of the hidden layer neurons, classification prediction accuracy of test set samples would not increase significantly consequently. As for the second classifier, from the program running results of the classifier using SVM, it can be seen that the classifier can obtain higher classification accuracy than the former. In other words, the number of misjudged samples by using SVM model was a little lower than that of ELM. In view of the classification prediction accuracy, accuracy of ELM was slightly lower than that of SVM actually, and for the goal of getting a higher classification accuracy determination of the number of hidden layer neurons using ELM classifier needs to run program repetitively, and it may take a very long time. However, the number of called function and optimal parameters of ELM classifier were much less than that of SVM, so its parameter setting process was relatively simpler and faster.
In summary, research results in this paper indicate that AMT method can well describe the textural feature of SEM images of microscopic surface topography of the composite. AMT method is capable of expressing discriminating and quantitative characterizations of texture complexity of SEM images. The feasibility and effectiveness of textural feature extraction using AMT method has been proved. According to extracted AMT spectra of SEM images of wheat straw/polypropylene composites in five different aging periods, two classifiers based on ELM and SVM were applied to identify those different aging stages. For the two models SVM model performed better than ELM model in general according to the sensitivity and
The authors declare that there is no conflict of interests regarding the publication of this paper.
This work was supported by the Funded Projects of “Twelfth Five-Year” National Science and Technology Support Plan (Grant no. 2011BAD20B03-02), Basic Scientific Research Special Funds for the Central Universities (no. KYZ200921), and Open Project of Jiangsu Key Laboratory of Large Engineering Equipment Detection and Control (no. JSKLEDC201204). Particularly, sincere and deep thanks are due to Professor K. Kvaal who gave the authors so much guidance and inspiration continually, and it has never been changing that his help finds the authors well and timely always.