Geometric Deep Lean Learning: Deep Learning in Industry 4.0 Cyberxe2x80x93Physical Complex Networks In the near future, value streams associated with Industry 4.0 will be formed by interconnected cyberxe2x80x93physical elements forming complex networks that generate huge amounts of data in real time. The success or failure of industry leaders interested in the continuous improvement of lean management systems in this context is determined by their ability to recognize behavioral patterns in these big data structured within nonEuclidean domains, such as these dynamic sociotechnical complex networks. We assume that artificial intelligence in general and deep learning in particular may be able to help find useful patterns of behavior in 4.0 industrial environments in the lean management of cyberxe2x80x93physical systems. However, although these technologies have meant a paradigm shift in the resolution of complex problems in the past, the traditional methods of deep learning, focused on image or video analysis, both with regular structures, are not able to help in this specific field. This is why this work focuses on proposing geometric deep lean learning, a mathematical methodology that describes deepleanlearning operations such as convolution and pooling on cyberxe2x80x93physical Industry 4.0 graphs. Geometric deep lean learning is expected to positively support sustainable organizational growth because customers and suppliers ought to be able to reach new levels of transparency and traceability on the quality and efficiency of processes that generate new business for both, hence generating new products, services, and cooperation opportunities in a cyberxe2x80x93physical environment.

Industrial Transfer Learning: Boosting Machine Learning in Production In the field of production, machine learning offers great potentials to develop innovative solutions for optimization or automation. However, it faces challenges with regard to the availability of data and the high training effort of the learning models in the event of changes in the production process. In this paper, we address these challenges by introducing deep transfer learning for production. We demonstrate its potentials and benefits in a real application for predictive quality in injection molding and propose a novel approach for the continual training of neural networks across manufactured products. By creating a neural network that leverages knowledge from previous products without forgetting them, the approach shows better learning rates and more accurate predictions while requiring much less data for training. Our code is publicly available1 to reproduce our results and build upon them.