Pattern recognition for the analysis of polymeric materials

Abstract

A new method of polymer classification is described involving dynamic mechanical analysis of polymer properties as temperature is changed. The method is based on the chemometric analysis of the damping factor (tan delta) as a function of temperature. In this study four polymer groups, namely, polypropylene, low density polyethylene, polystyrene and acrylonitrile-butadiene-styrene, each characterised by different grades, were studied. The aim is to distinguish polymer groups from each other. The polymers were studied over a temperature range of -50 degrees C until the minimum stiffness was reached, tan delta values were recorded approximately every 1.5 degrees . Principal components analysis was performed to visualise groupings and also for feature reduction prior to classification and clustering. Several clustering and classification methods were compared including k-means clustering, hierarchical cluster analysis, linear discriminant analysis, k-nearest neighbours, and class distances using both Euclidean and Mahalanobis measures. It is demonstrated that thermal analysis together with chemometrics provides excellent discrimination, representing a new approach for characterisation of polymers.

Polymer Informatics

“…Lukasiak et al reported the use of several classification techniques, such as PCA, k-means and hierarchical clustering, linear discriminant analysis, k-nearest neighbors as well as distance metrics using Euclidean and Mahalanobis measures on data generated by Dynamic Mechanical Analysis (DMA) of several types of polymers (polypropylene, LDPE, polystyrene, acrylonitrilebutadiene-styrene) of several different grades [121]. In their paper, the authors determined the damping factor (tan δ) of the polymers as a function of temperature and showed that this data, in combination with several of the machine learning techniques listed above, can be used to classify polymers into types and grades.…”

One‐class classifiers

“…Further details have been described elsewhere [16][17][18][19][20][21]. The dataset consists of the thermal profiles of 293 samples, involving monitoring the change in physical properties as they are heated.…”

Disjoint hard models for classification

“…The method is illustrated using the thermal profiles of nine groups of polymers studied by Dynamic Mechanical Analysis [45][46][47][48][49][50][51] representing a complex problem where there are nine classes. The structure of each class is often quite different, as each group of polymers consists of several grades or subgroups, hence is not homogeneous.…”