Image-based manufacturing analytics: Improving the accuracy of an industrial pellet classification system using deep neural networks

Abstract

Manufacturing analytics is of paramount importance in many plants today, and its relevance increases in the current big data context of Industry 4.0. The fields of statistics, chemometrics, and machine learning are expected to provide tools that effectively handle many of the characteristics of industrial data. In this paper, the task of image-based product classification is considered. This is a supervised learning problem where the input is an image and the output is a unique label attributed to the image from a finite set of labels corresponding to the available product classes. This is a prevalent and highly relevant industrial challenge and recent developments in deep learning have proven to be successful in increasing the image classification accuracy, providing state-of-the-art results. Thus, in this work, we leverage deep neural networks' (DNN) ability to automatically learn features from images and test their performance in a real industrial context for predicting the pellet shape. In order to accelerate the training of DNN, transfer learning is employed and a network previously developed for one task is adapted to predict pellet shape. Furthermore, other less complex techniques such as partial least squares discriminant analysis (PLS-DA) and random forests (RF) are also explored in order to assess the benefits of adopting DNN as opposed to current classifiers.An industrial image classification case study was utilized to compare PLS-DA, RF, and DNN models. Compared to the in situ classification system currently in use, increasingly complex models (PLS-DA and RF) were able to better utilize the same pre-defined features and improve prediction accuracy significantly. DNN obtained the highest accuracy on the independent test set, with the advantages of not requiring the a priori computation of image features since they are directly extracted from the raw images. Moreover, by visualizing the output of some layers of the DNN, it is possible to verify that activations occurred in regions that are indeed meaningful for the classification tasks, further supporting that DNN were effectively modelling the relevant features of the pellet.