Ceramic powder characterization by multilayer perceptron (MLP) data compression and classification

A neural network approach for pattern classification has been explored in the present paper as part of the recent resurgence of interest in this area. Our research has focused on how a multilayer feedforward structure performs in the particular problem of particle characterization. The proposed procedure, after suitable data preprocessing, consists of two distinct phases: in the former, a feedforward neural network is used to obtain an image data compression. In the latter, a neural classifier is trained on the compressed data. All the tests have been conducted on a sample constituted by two different typologies of ceramic particles, each characterized by a different microstructure. The sample image of different particles acquired and directly digitalized by scanning electron microscopy has been processed in order to achieve the best conditions to obtain the boundary profile of each particle. The boundary is thus assumed to be representative of the morphological characteristics of the ceramic products. Using the neural approach, a classification accuracy as high as 100% on a training set of 80 sub-images was achieved. These networks correctly classified up to 96.9% of 64 testing patterns not contained in the training set.