Visual Analysis of Deep Learning Methods for Industrial Vacuum Metalized Film Product

Abstract

Extract information to support decisions in a complex environment as the industrial is not an easy task. Information technologies and cyber-physical systems have provided technical possibilities to extract, store, and process many data. In parallel, the recent advances in artificial intelligence permit the prediction and evaluation of features and information. Industry 4.0 can benefit from these approaches, allowing the visualization of process, feature prediction, and model interpretation. We evaluate the use of Machine Learning (ML) to support monitoring and quality prediction of an industrial vacuum metalization process. Therefore, we proposed a semantic segmentation approach to fault identification using images composed of optical density (OD) values from the vacuum metalized film process. Besides that, a deep neural network model is applied to product classification using the segmented OD profile. The semantic segmentation allowed film regions analysis and coating quality associations through their class and format. The proposed classifier presented 86.67% of accuracy. The use of visualization and ML approaches permits systematical real-time process monitoring that reduces time and material waste. Consequently, it is a promising approach for Industry 4.0 on monitoring and maintenance support.