Abstract
The production of the nonwoven fabric is a particular process that requires to avoid any kind of defect and contamination. High-resolution camera systems and different image analysis techniques are currently in use. Nevertheless, the common procedure requires a manned intervention. In this work we propose, using both metadata and images of the defects provided by the vision system, a joined application of machine learning, and deep learning techniques to completely automate the severe defect image identification in order to obtain high-quality products and reduce machine stops. By means of Principal Component Analysis, Hotelling’s T-Squared, and Q residuals we were able to use the metadata to identify and remove anomalous samples; then, by applying the Partial Least Squared/Discriminant Analysis we performed feature selection on the metadata variables. Metadata pre-processed in such way and related images are used as an input of a combination of two different subnetworks: a Convolutional Neural Network to analyze the images and a Multi-Layer Feedforward Neural Network to analyze the metadata.
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Calderisi, M., Galatolo, G., Ceppa, I., Motta, T., Vergentini, F. (2021). Automatic Defect Recognition in Nonwovens Using Images and Metadata Analysis—A Deep Learning Approach. In: Dingli, A., Haddod, F., Klüver, C. (eds) Artificial Intelligence in Industry 4.0. Studies in Computational Intelligence, vol 928. Springer, Cham. https://doi.org/10.1007/978-3-030-61045-6_9
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